Factors Related to the Intensity of Occupational Therapy Utilization in a Geriatric Chronic Care Setting
Bibliographic record
Abstract
BACKGROUND: There is very little known about the intensity of occupational therapy service provision in relation to client characteristics of a geriatric chronic care population. A model was utilized to study demographic and clinical factors associated with the intensity of occupational therapy utilization. METHOD: A retrospective correlational design was carried out using secondary analysis of occupational therapy workload data merged with selected variables from the Minimum Data Set (MDS) at Baycrest Centre for Geriatric Care, Toronto, Ontario and included a sample of 168 clients receiving occupational therapy. The outcome measure used was the total number of minutes of occupational therapy service provided. RESULTS: Having a pressure relieving device for the chair and being active more than one third of waking hours were significantly associated with the intensity of occupational therapy utilization. The clients received a greater amount of time in indirect therapy compared with the amount of time which they received in direct care. PRACTICE IMPLICATIONS: The method used to examine occupational therapy service utilization developed in this research facilitates the understanding of occupational therapy resource use based on client characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".