Credo pillar five
Universities and research
Universities, PhD and Masters candidates, health services and research groups are interested in this app and in the work behind it. We know that, and we welcome it. Nurse Practitioner education, advanced practice, supervision, evidence burden, CPD, wellbeing and workforce transition are all under-researched, and the profession is the poorer for it.
Welcome is not the same as unconditional. Anything we consider has to meet the credo, and three conditions come before any conversation about data.
Three conditions
It has to answer the credo question
Where does this sit in the credo? Which pillar does it serve, and whose question does it answer? That is the first thing we read, and it is the thing that decides whether the conversation goes further.
Members own their record, and members decide
A member’s portfolio, CV, clinical narratives, employment history, communications and AI conversations are theirs. A research request does not reach them. The research opt-in that exists inside the app today covers de-identified survey answers and wellbeing pulses only — it does not authorise research use of anything else. A study wanting more would need consent appropriate to that study, given by the members themselves.
Research access and partnership are paid
This is a commercial arrangement, scoped and quoted per project once a request has been assessed against the credo. We do not publish a rate, because the work differs every time.
Our practice, our outcomes, and our patients generate data that universities, PhD candidates, and researchers will want. That's welcome — but not unconditional.
Before we hand over data, the request has to answer one question: where does this sit in the credo? Whose question does it actually answer, and which pillar does it serve — the patient, the profession, the evidence base, something concrete? Research that treats NPs as a convenient dataset, without engaging with what we're building or why, doesn't get access by default. Research that does engage with it should find us genuinely willing partners.
Any study would additionally require a protocol, university ethics review, participant information and consent appropriate to that study, a data-management plan, a written agreement with EndorseMe, and confirmation of the applicable Australian privacy, health-records and institutional requirements.
What could be studied
We mapped this ourselves, so that a researcher can work out whether their question fits before writing to us. It is a map of the landscape, not an offer, and not an index of fields to request.
- Current
- Stored or directly captured by the application or schema today.
- Derived
- Calculable from existing records without asking a new question.
- Proposed
- Not reliably collected now. Would require a new, optional and ethically approved collection.
- Restricted
- Identifiable, sensitive, third-party or patient-related data that should not enter a researcher dataset.
Listed below means technically represented in the repository. It does not mean that every field is populated, or that the data may be used for research. Before any study, a live data dictionary would have to be produced, field population and provenance confirmed, missingness assessed, and every derived measure validated against the current application code.
8 themes. Open any one to see what sits under it.
The endorsement journey
The path itself: university and program, placement, the stages a candidate moves through, and how long each one actually takes.
5 current · 4 derived · 5 proposed · 3 restricted
The endorsement journey
The path itself: university and program, placement, the stages a candidate moves through, and how long each one actually takes.
5 current · 4 derived · 5 proposed · 3 restricted
Drawn from sections 5, 6, 16 and 28 of the report.
- Pathway — candidate, overseas or endorsed — and transitions between them.
- The university and NMBA-approved program selected, with start and expected completion dates.
- Overseas qualification assessment stage, outcome, bridging requirements and Pathway 2 unit mapping.
- Program placement dates, hours, setting and supervisor role, held separately from advanced-practice employment hours.
- Onboarding step, candidacy stage, progression milestones, and self-recorded submission and endorsement dates.
- Time from account creation to onboarding completion, and between each subsequent milestone.
- Time to 5,000 eligible hours, to evidence completion, to application drafting, and to self-recorded endorsement.
- Where journeys stall, and how the set of missing items changes over time.
- Differences by pathway, geography, program or work setting, where the sample size permits.
- Entry pathway and place type: Commonwealth-supported, fee-paying, scholarship, employer-funded or self-funded.
- Study load, interruption, deferral, withdrawal and re-enrolment, with participant-defined reasons.
- Delivery mode, and perceived preparedness in advanced assessment, diagnostic reasoning, pharmacology, therapeutics, collaboration, leadership and research.
- Placement availability, allocation delay, travel, accommodation, cost and conflict with paid employment.
- Participant-reported application outcome, effort, fees and sources of advice, gathered at defined timepoints rather than by continuous observation.
- EndorseMe does not decide eligibility or endorsement. Readiness, gap and coverage measures are product outputs and must never be presented as regulatory outcomes.
- Reviewer notes, AENP-40 content and application reference numbers stay out.
- Unit-level records and grades are educational records. They need explicit linkage consent and university approval, and institution comparisons must not become unvalidated rankings.
Hours, evidence and the standards
The 5,000-hour requirement and everything assembled to evidence it: employment, verification, portfolio items, and coverage of the NMBA standards.
5 current · 6 derived · 6 proposed · 5 restricted
Hours, evidence and the standards
The 5,000-hour requirement and everything assembled to evidence it: employment, verification, portfolio items, and coverage of the NMBA standards.
5 current · 6 derived · 6 proposed · 5 restricted
Drawn from sections 7, 8, 11 and 12.
- Employment periods with role, practice area, setting, dates, hours per week and FTE, and whether the period was part of an NP program.
- Extended leave, supervision model, advanced-practice description, evidence domains, and clinical and autonomous practice percentages.
- Shift logs, and statements of service with weekly and total hours, leave periods and signatory status.
- Evidence items with category, certification status, certifier and expiry.
- Evidence mapped to the four NMBA Nurse Practitioner Standards for Practice and their twelve practice statements.
- Total, verified and pending advanced-practice hours, and eligible hours inside a study-defined observation window.
- Hours excluded because they were NP-program practice, and hours adjusted for extended leave.
- Number of employers, role changes, sector changes, employment gaps and concurrent roles.
- Time to reach each hour milestone.
- Verification completion rate, time from request to receipt, and agreement between self-reported and employer-verified hours.
- Evidence accumulation and certification rates, and coverage by standard and practice statement over time.
- Employer sector, funding source and service model, in place of the employer itself.
- Protected study time, and employer support for placement, supervision, evidence gathering and endorsement.
- The time and out-of-pocket cost a candidate spends locating, certifying and organising evidence.
- Employer reason for delay or refusal, and employer familiarity with the evidence requirements.
- Perceived difficulty of obtaining each evidence type, and reasons evidence is missing, rejected or expired.
- Reviewer agreement and inter-rater reliability for structured standards mappings, and the reasons for disagreement.
- A research export replaces employer and site with sector, jurisdiction and remoteness group, and exact dates with month, quarter or elapsed time.
- Supervisor identity and contact details are removed. Letters, signatures, employer addresses and generated statement text are not researcher data — structured completeness and elapsed-time measures are.
- Program placement hours are never combined with the separate advanced-practice hours requirement in analysis or in reporting.
- Portfolio coverage must not be converted into a competence, endorsement-likelihood or patient-safety score, and app-generated completeness figures are decision support, not AHPRA decisions.
- Uploaded files, identity documents, transcripts, reference letters and raw portfolio text are not provided.
Supervision, referees and mentoring
Who supports a candidate, on what terms, how often, and what happens to the people who volunteer to do it.
6 current · 6 derived · 7 proposed · 3 restricted
Supervision, referees and mentoring
Who supports a candidate, on what terms, how often, and what happens to the people who volunteer to do it.
6 current · 6 derived · 7 proposed · 3 restricted
Drawn from section 13.
- Candidate and supervisor relationship type, organisation, position, dates and status.
- Supervisor approvals and feedback recorded against the practice statements.
- Referee request type, checklist, status, reminder and response dates.
- Supervisor statements covering role period, organisation and examples.
- Mentor relationships, including mode and whether the arrangement is paid or unpaid.
- The mentoring-practice registry, which carries its own separate research and contact consent.
- Whether a candidate has access to a supervisor or a mentor at all.
- Time from invitation to acceptance, and relationship duration and continuity.
- The mix of remote, in-person and hybrid supervision.
- The mix of paid, placement, self-funded and goodwill arrangements.
- Supervisor and referee response and completion rates, and feedback frequency and statement coverage.
- Mentor capacity set against unmet candidate demand.
- Frequency, duration and protected time for supervision.
- Supervisor preparation, training and years supervising.
- Quality of feedback, trust, psychological safety and goal alignment.
- What supervision actually consists of: observation, case review, prescribing review, assessment feedback, professional support.
- Barriers to finding a supervisor or a mentor in the first place.
- Mentor workload, cost, organisational support and burnout.
- Why a relationship ended, defined by both parties rather than inferred from whether it completed.
- Supervisor, referee and mentor names, contact details, signatures, IP addresses, exact organisations and free-text comments are removed.
- A member consenting does not authorise research use of a third party contribution. A supervisor’s words are the supervisor’s to give.
- Network analysis carries a high re-identification and professional-reputation risk, and needs consent from every person represented in it.
CPD, capability and the professional record
Continuing professional development against the AHPRA registration year, the reflection that goes with it, and the CV and career record it feeds.
5 current · 6 derived · 7 proposed · 5 restricted
CPD, capability and the professional record
Continuing professional development against the AHPRA registration year, the reflection that goes with it, and the CV and career record it feeds.
5 current · 6 derived · 7 proposed · 5 restricted
Drawn from sections 14 and 15.
- Activity title, date, type, provider, claimed hours and registration year, and whether the member claims it toward NP-specific hours.
- The four NMBA evidence elements: learning need, action plan, reflection and outcomes.
- Attestation status, method and time, and supervisor verification.
- Annual required and completed hours against the AHPRA registration year, and the planned professional development diary.
- CV documents, sections, versions, imported files, AI suggestions and exports, and professional-log entries.
- Total annual CPD and NP-specific hours, and completion against the applicable registration year.
- Activity, provider and topic mix, and the proportions that are formal, informal, self-directed or part of a series.
- Evidence and reflection completeness, and time from learning need to activity and from activity to reflection.
- Planned-to-completed conversion, and verified against self-attested activity.
- Repeated learning needs, and change in self-assessment over time.
- Number and type of CV revisions, time to complete core sections, and suggestion acceptance rate.
- The direct cost, travel and unpaid time that CPD actually takes.
- Employer funding and protected CPD time.
- Delivery mode, accessibility and rural availability.
- Perceived quality, relevance and transfer to practice, and sustained practice change at follow-up.
- Pre- and post-activity knowledge, confidence or skill using a validated assessment.
- Barriers to CPD access and completion, and preferred formats, providers and topics.
- Career aspirations, job-search outcomes, and employment at 6, 12 and 24 months after endorsement.
- Reflection text can reveal personal, clinical or workplace vulnerability, and is excluded from routine exports.
- Course title and provider combinations can identify a narrow speciality. Broad domains are used for reporting.
- Claimed hours are member attestations unless independently verified, and planned professional development is not evidence of attendance, completion or CPD credit.
- Raw CV text, imported documents and employment chronology are highly identifying.
- Uptake of an AI suggestion is a measure of product behaviour, not of writing ability or professional quality.
Clinical practice and case exemplars
EndorseMe is a professional-record platform, not an electronic health record, and it must not become a store of identifiable patient data. What is studiable here is the practitioner and the service, in aggregate — never the patient.
4 current · 4 derived · 6 proposed · 5 restricted
Clinical practice and case exemplars
EndorseMe is a professional-record platform, not an electronic health record, and it must not become a store of identifiable patient data. What is studiable here is the practitioner and the service, in aggregate — never the patient.
4 current · 4 derived · 6 proposed · 5 restricted
Drawn from sections 9 and 10.
- Broad clinical or practice area and setting.
- Self-recorded clinical activities and advanced-practice evidence categories.
- De-identified case exemplars carrying presenting problem, assessment findings, differential diagnosis, management plan, outcome and reflection.
- Broad complexity and autonomy markers, and mapping of that evidence to the practice statements.
- Number and completion rate of case exemplars.
- Distribution of broad complexity indicators, and breadth of standards coverage.
- Time from case date to documentation.
- The proportion excluded from endorsement evidence because the case was already used in the Master’s program.
- Practitioner- or service-level aggregates only, and only where a protocol justifies them: consultation count bands, new against follow-up proportion, and the in-person, telehealth, outreach and home-visit mix.
- Broad presenting-problem or speciality categories, and age-group and population-group distributions in broad bands.
- Rural, remote, regional, metropolitan and underserved-population service.
- Diagnostic-test requests, medicine-management activity, referrals and care coordination as aggregate frequencies, with interprofessional consultation and escalation patterns.
- Model-of-care characteristics, funding, governance, credentialling and scope.
- Patient-reported experience and outcome measures, which would require a separate protocol with the patients themselves.
- No names, initials, full dates of birth, addresses, Medicare numbers, medical-record numbers, exact dates, images, audio or unique case details.
- No patient-level linkage on a nurse participant’s consent alone, and no inference of diagnosis from portfolio text.
- Raw narratives, attachments and voice recordings are excluded from routine exports. A de-identification flag does not prove that the re-identification risk is acceptable, and automatic scrubbing is a safeguard rather than a guarantee — a human disclosure review comes before any approved quotation.
- Rare conditions, rare procedures and small services need additional disclosure-risk review.
- Patient outcomes described by the practitioner who provided the care are not independently verified clinical outcomes.
Wellbeing, equity and the workforce
What the journey costs the person making it, who is and is not making it, and what happens to the workforce at the other end.
5 current · 3 derived · 4 proposed · 5 restricted
Wellbeing, equity and the workforce
What the journey costs the person making it, who is and is not making it, and what happens to the workforce at the other end.
5 current · 3 derived · 4 proposed · 5 restricted
Drawn from sections 3, 4, 17, 22 and 23.
- Pathway type, study state, country of qualification, area of practice, current employer, work setting and years-of-experience band where supplied.
- Registration and endorsement status, professional indemnity insurance status, and recency-of-practice period and compliance state.
- A de-identified fortnightly mood and confidence pulse, carrying only a coarse pathway cohort.
- Consented, de-identified survey answers.
- Public job posts by title, organisation, location, speciality and employment type.
- Cohort composition by pathway and broad geography, where the sample size permits.
- Wellbeing and confidence trajectories from the fortnightly pulse.
- Access duration and uptake by pathway, in aggregate.
- An optional equity module, designed with the communities it asks about: age band rather than birth date; sex and gender as separate questions; Aboriginal and Torres Strait Islander identity using an institutionally approved question under Indigenous data-governance arrangements; cultural background and language; remoteness; disability, neurodivergence and caring responsibilities; financial pressure and study debt; first-in-family status. Every item optional, with "prefer not to say" throughout.
- Validated measures for burnout, moral distress, psychological and cultural safety, work-study and work-family conflict, and exposure to bullying, discrimination and racism.
- Workforce measures: vacancy volume by jurisdiction, remoteness, speciality and sector; time to fill; time from endorsement to first NP role; underemployment and mismatch between authorised and actual scope; retention, mobility and turnover.
- Study and endorsement costs by category, including placement travel, accommodation and foregone income.
- Exact birth date, residential address, small-community location and rare speciality-and-employer combinations can re-identify a participant.
- Small cells must be suppressed or combined — especially for Aboriginal and Torres Strait Islander participants, overseas cohorts, remote communities and uncommon clinical specialities.
- Demographic fields must never be used to rank eligibility, professional competence or employability, and no employability or candidate-ranking score may be built.
- Sensitive wellbeing questions need a distress and disclosure protocol agreed before they are asked, every item skippable, and honesty about whether anyone is reading the answers. The app does not monitor for crisis and must not imply that it does.
- Billing email, payment method and individual payment state do not enter a university dataset, and financial answers must be declinable.
App use and AI interaction
How the product is actually used, and how members interact with Matron — studied from the records people create, not from watching them.
6 current · 5 derived · 6 proposed · 5 restricted
App use and AI interaction
How the product is actually used, and how members interact with Matron — studied from the records people create, not from watching them.
6 current · 5 derived · 6 proposed · 5 restricted
Drawn from sections 18, 19, 20 and 21.
- Account, profile and record creation and update timestamps, and onboarding progression.
- Export and download counts, and notification sent, read and acknowledged times.
- App feedback with rating, type, page and device class, and support-ticket category, status and resolution time.
- Matron session status, and tool name, model, channel, latency and token counts in audit events.
- Member-confirmed memories, agent preferences, and approval requests with their decisions.
- Community access codes, speciality hubs, professional development events and diary selections.
- Feature adoption inferred from the records people create, not from clickstream surveillance — no per-member usage-event instrumentation exists.
- Time to a first evidence item, CPD entry, employment period, CV export or portfolio export.
- Record completion and abandonment, and retention on coarse active-month measures.
- Feedback volume, recurring themes and resolution time, and device-class and browser issues in aggregate.
- AI feature adoption, response latency, error and fallback rates, proposal approval and decline rates, and how often a response was source-supported.
- A consent-based research event stream with a small, published event dictionary: session bands, task start, task completion and error events.
- Validated usability, trust and accessibility measures, and digital literacy, device access and connectivity.
- Explicit, separate consent for AI-interaction research, retaining task intent and correction categories rather than raw prompts.
- Accuracy and safety evaluation against expert-built test cases, with human review for fabrication, overclaim, bias and citation quality.
- Whether a suggestion was accepted, edited, rejected or later reversed.
- A randomised or stepped-wedge evaluation of a clearly described product change, with prior consent where required.
- No keystrokes, clipboard contents, screen recordings, precise or address-derived location, cross-site behaviour or device fingerprints are collected for research.
- Operational rate limits, security logs, tokens and push endpoints are not research observations.
- Raw prompts, responses, memories and chat transcripts are excluded — they can contain clinical, employment, identity or vulnerability information.
- Member records must not be used to train or fine-tune a model without specific, prominent consent and an approved protocol.
- Product engagement must not be presented as professional motivation, competence or endorsement readiness, and AI output is not an independent assessment of competence or regulatory compliance.
Consent, governance and how a study would run
The machinery a study needs around it: what members have actually consented to today, and the separation, provenance and disclosure control any serious design would require.
4 current · 6 proposed · 7 restricted
Consent, governance and how a study would run
The machinery a study needs around it: what members have actually consented to today, and the separation, provenance and disclosure control any serious design would require.
4 current · 6 proposed · 7 restricted
Drawn from sections 1, 2, 24, 25, 27, 29, 31 and 32.
- Research opt-in status, with consent, withdrawal and preference-update dates.
- Survey title, purpose, lifecycle status, creator, approver and approval time, and question wording, order and answer types.
- De-identified survey answers and the fortnightly pulse, carrying only a coarse pathway cohort.
- One-way respondent hashes for duplicate prevention, with the salts held server-side.
- A study registry: protocol and version, institution and investigators, ethics approval and expiry, research questions, approved variables and sources, permitted linkages, consent version, retention and destruction dates, disclosure controls, and the named data custodian.
- Separate consent choices for surveys, longitudinal follow-up, linkage to selected records, interview contact, publication of de-identified quotations, and recontact for another study — study-specific rather than one permanent switch.
- A research-safe transformation layer: only approved variables copied into a separate environment, member identifiers replaced with study-specific random ones, dates, geography, employer, university and speciality generalised, free text removed by default, and minimum-cell rules and disclosure review applied to every export.
- Provenance and verification labels on every measure — member, supervisor, employer, university, system or AI, and draft, attested, verified or derived — so the sources are never conflated.
- Qualitative workflows held in a research environment outside the operational database, not mixed into portfolio storage merely because the app can hold free text and audio.
- External linkage to university, employer, registration, CPD-provider or administrative data only through an authorised custodian, with the minimum necessary identifiers held outside the analysis dataset.
- The member account identifier never goes into an analysis file, and operational duplicate-prevention hashes are not anonymous identifiers while the platform holds the salts.
- Consent records and contact details are held separately from research observations.
- Names, email addresses, phone numbers, registration numbers and exact employer or university contacts are direct or strong indirect identifiers.
- Credentials, invitation and verification tokens, OAuth codes, push endpoints and private-link tokens are never research data.
- Waitlist communication consent is not research consent, and marketing consent is not research consent.
- Security logs, IP addresses, user agents, audit metadata and administrator identity are operational records, not participant data.
- Refusing or withdrawing must never affect a member’s account access, support, subscription, community access or endorsement tools.
What is not collected or released
This list is not a negotiating position and it is not shortened for a good project. It exists because members, their patients, their supervisors and their employers are all represented in this data, and only one of those four is our member.
- Identifiable patient information or raw patient records.
- Raw case attachments, images, voice recordings or unique case narratives.
- Full identity documents, transcripts, certificates, letters, signatures or CVs.
- Passwords, auth tokens, OAuth grants, private-link tokens, push endpoints or API secrets.
- Exact residential or workplace addresses.
- Registration numbers in an analysis dataset.
- Supervisor, referee, mentor, patient, employer-contact or university-contact identity without that person's consent.
- Raw AI prompts, responses, memories or chat transcripts.
- Raw support tickets, feedback messages, admin notes or email bodies.
- Individual billing status or payment-method details.
- Exact device fingerprints, IP addresses or cross-site tracking.
- Hidden behavioural surveillance, keystrokes, clipboard data or screen recordings.
- Unvalidated scores predicting competence, patient safety, endorsement success or employability.
- Data collected “just in case” without a research question, retention period and approved use.
Please do not send patient information, identifiable member data or any clinical content through this form. If a request proceeds, what is shared, how it is de-identified, who approves it and on what commercial terms are all agreed in writing first.