Enhancing reflective practice through online learning: impact on clinical practice
Bibliographic record
Abstract
PURPOSE: Traditionally, radiographers and radiation therapists function in a workplace environment that is protocol-driven with limited functional autonomy. The workplace promotes a culture of conformity and discourages practitioners from reflective and critical thinking, essential attributes for continuing learning and advancing workplace practices. As part of the first author's doctoral study, a continuing professional development (CPD) educational framework was used to design and implement an online module for radiation therapists' CPD activities. The study aimed to determine if it is possible to enhance healthcare practitioners' reflective practice via online learning and to establish the impact of reflective learning on clinical practice. MATERIALS AND METHODS: The objectives of the online module were to increase radiation therapists' knowledge in planning for radiation therapy for the breast by assisting them engage in reflective practice. The cyclical process of action research was used to pilot the module twice with two groups of volunteer radiation therapists (twenty-six participants) from Australia, New Zealand and Canada. RESULTS: The online module was evaluated using Kirkpatrick's four-level evaluation model. Evidence indicated that participants were empowered as a result of participation in the module. They began reflecting in the workplace while assuming a more proactive role and increased clinical responsibilities, engaged colleagues in collaborative reflections and adopted evidence-based approaches in advancing clinical practices. CONCLUSION: The study shows that it is possible to assist practitioners engage in reflective practice using an online CPD educational framework. Participants were able to apply the reflective learning they had developed in their workplace. As a result of their learning, they felt empowered to continue to effect changes in their workplace beyond the cessation of the online module.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".