Evaluation of a Sleep Knowledge Translation Strategy for Occupational Therapists Working with Persons who have Dementia
Bibliographic record
Abstract
Introduction: Sleep deficiency is a significant, largely overlooked issue for persons with dementia (PWD), and is associated with physical and mental health problems, increased caregiver burden, and increased likelihood of institutionalization. Despite the high prevalence of sleep deficiency in PWD, most health care professionals lack knowledge of the relationship between sleep problems and dementia. This project aimed to determine the feasibility of an archived online presentation, a knowledge translation (KT) strategy to increase therapists’ understanding of the impact of blue-spectrum light on sleep in PWD. Method: Therapists who participated in a previous sleep and dementia survey were recruited via email. Participants completed a pre-knowledge test, accessed an online presentation regarding the relationship between sleep and light, and completed a post-test. Results: On average there was a 22% improvement in knowledge scores and participants were positive about the KT strategy being accessible, applicable, and evidence based. Conclusion: For a sample of therapists self-identified as specializing in geriatric rehabilitation, online audio-visual resources appear to be a feasible KT strategy to disseminate information and increase occupational therapists’ knowledge regarding the evidence-based relationship between blue-spectrum light and sleep in PWD. Further study is required to determine if this increased knowledge translates to practice 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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".