Portfolios with Evidence of Reflective Practice Required by Regulatory Bodies: An Integrative Review
Bibliographic record
Abstract
Purpose: Regulatory bodies impose continuing professional development (CPD) requirements on health care professionals (HCPs) as a condition for license revalidation. Many regulatory bodies require annual evidence of CPD activities that are informed by reflective practices, guided by learning plans, and compiled into a portfolio. The purpose of this integrative review is to summarize the literature discussing how regulatory bodies use portfolios with evidence of reflection for licensure renewal. Method: We reviewed English-language articles published until May 2020 discussing evidence of CPD and reflective practice in portfolios in the context of licensure renewal. Results: We located 17 articles for the review. None reported or measured outcomes beyond submission of reflective evidence. Sixteen articles (93%) included information about passive learning resources that regulatory bodies provided to help guide HCPs’ reflective learning activities. HCPs’ feedback about using reflective learning activities indicated mixed opinions about their utility. Conclusions: Few publications reported how jurisdictions expected HCPs to provide evidence of reflective practices for licensure renewal. None of the regulatory bodies required evidence regarding the impact of reflective practice on patient or organizational outcomes. HCPs reported both benefits and challenges of a mandated reflective process.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".