Innovation and Competency Development in Occupational Therapy Fieldwork During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Occupational therapy clinical education was disrupted because of the COVID-19 pandemic. This introduced both challenges and opportunities in clinical fieldwork education and created a naturalistic opportunity to study the innovations that occurred. Purpose: To identify and describe fieldwork education innovations that occurred during the COVID-19 pandemic and understand how these clinical learning contexts impacted competency development in occupational therapy learners. Method: A qualitative multi-case study methodology was used. The participants ( N = 28) were occupational therapy learners and preceptors who self-identified as having participated in an innovative fieldwork placement during the pandemic either as a preceptor or learner. Data were collected via in-depth interviews and analyzed to identify cases of innovation. Within and across case analyses were conducted to describe innovations and competencies addressed. Findings: Three cases of fieldwork innovations were identified: (a) Virtual Care; (b) Intrapreneurship; and (c) Administration. The commonly addressed competency domains across the cases were OT Expertise, Excellence in Practice, and Communication and Collaboration. The competency domain, culture, equity, and justice, was only addressed in the virtual care case. Conclusion: Our findings indicate that innovative fieldwork placements can support competency development in occupational therapy; however, this development is complex and contextually based.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".