OCCUPATIONAL THERAPY PRACTICE WITH OLDER ADULTS VISITING EMERGENCY DEPARTMENTS
Bibliographic record
Abstract
Occupational therapists (OTs) are members of emergency departments (ED) teams, but the nature and scope of their practice is not well known. In 2007, a first descriptive study of OT practice in Quebec’s ED was published, in which authors documented the various aspects of the practice. In 2013, data were updated with a second survey. Then in 2018, individual interviews with 6 key informants were conducted, to further describe the professional practice of OTs in ED. Through the newsletter and the Website of the professional association, all OTs were contacted but only those working in ED were invited to respond (either by fax, mail, online or in person). Questionnaires covered the general context of practice, nature of interventions, assessment tools used, theoretical framework and the satisfaction of working in ED. Statistical descriptive and content analyzes were carried out. Results show the emerging and heterogeneous nature of this practice. OTs are working in ED as members of interdisciplinary teams. They spend over 90% percent of their time with older adults and the requested assessments covered — in order of frequency — cognitive abilities assessment, daily activities, safety upon return home, dysphagia and mobility. The sharpest change between the results of the two surveys suggests that in 2013, OTs use more diversified and more often standardized tools. In 2018, they are present in more regions of Quebec, compared to 2007 data. They also confirmed the clinical utility of OTs in ED for the assessment of the functional status.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".