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The role of medical regulations and medical regulators in fostering the use of eHealth data for strengthened continuing professional development (CPD): a document analysis with key informants’ interviews

2025· other· en· W6958959074 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicMarketing and Advertising Strategies
Canadian institutionsnot available
Fundersnot available
KeywordseHealthThematic analysisContext (archaeology)Digital healthChecklistQualitative researchQuality (philosophy)Key (lock)Certification

Abstract

fetched live from OpenAlex

Abstract Background In recent times, medical regulators have been taking measures to strengthen CPD requirements for medical practitioners. In particular, greater emphasis has been placed on CPD activities linked to workplace-based assessment, health outcomes measurement, and quality improvement. These activities require the use of health data, and eHealth data analytics is emerging as a digital solution to simplify tasks and processes. Although there is a growing interest and need for alignment between regulatory policies, impactful CPD activities, and digital health research and innovation, there is little or no research into the role that medical regulations and regulators are playing in fostering the use of eHealth data to strengthen CPD. Methods Medical regulations and CPD requirements of 5 selected countries (Australia, Canada, New Zealand, UK, USA) were collected and analysed using the systematic READ approach for qualitative health policy research. Online semi-structured interviews were conducted with 20 key informants from 13 medical bodies to validate findings and gather additional insights. Informants were purposively selected because of their direct involvement in policy development. The interviews were analysed using a hybrid approach of deductive and inductive thematic analysis. The COREQ checklist was used for reporting the findings. Results The documents analysed do not mention the use of eHealth data for CPD purposes or refer to it only as a potential data source for CPD completion and compliance. Participants corroborated the document analysis results and provided insights into the following themes: context and rationale of current policy choices and future policy development; roles, responsibilities, and functions of relevant medical bodies in fostering the use of eHealth data for strengthened CPD; barriers, challenges, and enablers for implementation. Conclusion Current medical regulations and CPD requirements do not foster the use of eHealth data for CPD purposes. Recommendations for future policy development are reliant on further research on key policy concepts, regulators’ internal organisational factors, and interorganisational collaboration within the CPD ecosystem. The alignment of all relevant CPD stakeholders is required to tackle existing barriers and challenges and promote digital health innovation in the CPD landscape. Medical regulators are called to play a leadership role in this scenario.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.500
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.301
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it