The role of reflection in implementing learning from continuing education into practice
Bibliographic record
Abstract
INTRODUCTION: Although the use of reflection to facilitate learning and its application in practice has been widely advocated, there is little empirical research to establish whether or not health professionals use reflection to integrate learning into clinical practice. Particularly troublesome is the lack of empirically based theory underlying strategies to promote reflection and understand factors that influence its use in translating learning into practice. Occupational therapists participated in this case study, in which reflection and implementation of learning from a short course into practice were examined using a multimethod approach. METHODS: In phase one (n = 41), quantitative data were collected from a practice survey, the Self-Reflection and Insight Scale (SRIS) and Commitment to Change (CTC) statements. In phase two (n = 33), follow-up CTC data were collected to quantify the extent of achievement of CTCs. Data from phases one and two were analyzed descriptively to inform the selection of interview participants (n = 10) in phase three of data collection. RESULTS: Two models were generated. One model describes when reflection was used, and the second model explains factors influencing its use. Participants used reflection before, during, and after the course, and reflection was influenced by a range of factors associated with the course, practice context, and the individual. DISCUSSION: The theory and models depicting the use of reflection may guide educators' use of reflective learning before, during, and after short courses.
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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 | 0.050 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".