Pre-implementation knowledge tool development for health services providers: A qualitative study of Canadian social workers
Bibliographic record
Abstract
Recent research has shown that social workers are particularly well placed to disseminate information about health-related social programs such as Canada’s Compassionate Care Benefit (CCB). Low uptake of the CCB may be due, in part, to a lack of knowledge. In response to this, we report on the development of CCB knowledge tools aimed specifically at social workers. Social worker-specific tools about the CCB were developed through a multi-step process. Using a computer-based qualitative messaging survey ( n = 16), social workers chose what they determined to be the most important messages needed to gain knowledge about the CCB. Using these chosen messages, draft tools were created and then refined for content and aesthetics using a focus group ( n = 8) and information from key informant interviews ( n = 3). Further research is needed to evaluate tool implementation effectiveness and use in practice. This study contributes to the understanding of knowledge translation strategies specific to social workers, and particularly those working in end-of-life 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 | 0.041 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".