Deployment of professional expertise during a period of disruption: A phenomenological study of rehabilitation clinicians
Bibliographic record
Abstract
RATIONALE: Practice context is known to influence the deployment of competencies. The COVID-19 pandemic created a major disruption in many practice contexts. The objective was to understand the lived experience of rehabilitation clinicians during a major disruption of their practice context, namely, the COVID-19 pandemic. METHODS: We used a longitudinal phenomenological design. Eligible clinicians were occupational therapists (OT), physical therapists (PT), physiotherapy technologists (Phys T.), speech-language pathologists (S-LP) and audiologists (AUD), working in the same rehabilitation workplace for at least 2 years before March 2020 (the pandemic). Clinicians who were reassigned to roles outside the field of rehabilitation were excluded. We conducted individual online interviews using a guide developed by the team with expertise and knowledge of the phenomenon. An interpretative phenomenological analysis was carried out. Results were discussed until the research team reached a consensus. RESULTS: A total of 32 clinicians participated in the study (12 OT, 5 PT, 5 Phys T., 7 S-LP, 3 AUD), working with a variety of clienteles and work settings, for an average of 11.7 ± 7.6 years in the same practice setting. A wide range of emotions (e.g. anger, sadness, guilt, fear, pride) reflected clinicians' experience during disruption. Professional expertise was perceived as being enhanced by disruption as clinicians stepped out of their comfort zone; this encouraged reflective practice and a recognition of the need to be more explicit about their decision-making process. Collaboration with colleagues was perceived as key for effective coping and deployment of adaptative expertise. CONCLUSIONS: A disruption in the practice context may have positive effects on professional expertise through the mobilization of reflective practice.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".