Clinical Instructors’ Experiences Working with and Assessing Students Who Perform below Expectations in Physical Therapy Clinical Internships
Bibliographic record
Abstract
Purpose: Clinical education is an integral component of the curriculum of all physical therapy (PT) entry-to-practice programmes in Canada. The literature indicates that working with and assessing students performing below expectations (SPBE) can be procedurally and emotionally difficult. Our study aimed to explore the experiences of clinical instructors (CIs) and the decision-making process involved when supervising SPBE in PT. Method: A total of 19 in-depth, semi-structured interviews were conducted with CIs, transcribed, and coded using qualitative thematic analysis. Results: Four factors appeared to be important for CIs when they were deciding how to assess SPBE: (1) features of student performance, (2) factors related to the CIs, (3) academic and clinical facility influencers, and (4) strategies and available resources. Concerns about safety and professional behaviour, a student’s clinical reasoning skills, and a lack of progression were key factors that CIs considered in recommending a final grade. CIs were more likely to recommend a failing grade if there was a series of repeated incidents rather than an isolated incident. Conclusions: We make several recommendations for the student, CI, and facilities to consider to better support and facilitate the process of working with SPBE in PT clinical education.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".