Photographic Media for Pain Expression: Situated Learning with Graduate-Entry Masters Students to Develop Skills in Applying Theory-to-Practice
Bibliographic record
Abstract
Entry-level healthcare practitioners must be able to engage in critical thinking, life long learning and be autonomous and accountable within the complex demands of healthcare in the 21st century. However, structuring learning opportunities to foster these skills within the pre-qualification curriculum can be challenging. To-date, little evidence exists in the literature to guide educators. This case report discusses how an elective module in the therapeutic use of digital photography for Master of Science in occupational therapy (MScOT) students was designed to enable students to develop an appreciation for, and ability in, scholarship and the application of theory-informed practice. The elective module is used as an example to illustrate the potential and relevance for Social Learning theory, Situated Learning theory and the concept of Most Knowledgeable Other (MKO) to guide capacity building in scholarship and theory-based practice. This collaboratively written student/faculty theoretical perspective, incorporating anecdotal evidence extracted from students’ learning assignments in the module, supports our conclusion that these types of learning modules may offer a useful vehicle in which situated learning can occur.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".