Job Demands of Occupational Therapy Clinical Placements: A Descriptive Study Using the Practicum Demands Measure©
Bibliographic record
Abstract
The purpose of this study was to group and analyze the Practicum Demands Measure© (PDM) data collected over 2 academic years and create a general profile of demands across practicum settings. The data will be used to guide faculty in the most suitable placement of students requiring accommodations for a disability. The study used a secondary analysis design to analyze the 538 participant PDM data collected over the 2017/2018 and 2018/2019 academic years. Most of the sampled students were in fieldwork Level I and worked in mental health settings. The students reported physical demands, such as lifting more than 5kg (65.7%), intermittent sitting (97.6%), and keyboarding (94.6%). They also reported physical environment characteristics, such as exposure to infectious disease (44.6%) and congested working areas (27.5%). Cognitive demands included instant recall (90%) and analytical and clinical reasoning (99.8%). Practicum demands in occupational therapy were similar across other health care profession student placements, such as nursing and physical therapy. Practicum demands need to be studied more extensively to optimize students’ opportunities for success for students requiring accommodations in varied clinical settings.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".