A Qualitative study assessing organisational readiness to implement caregiver support programmes in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To qualitatively explore factors affecting implementation of caregiver support programmes in healthcare institutions in a regional stroke system. DESIGN: A qualitative descriptive study with the Ontario Stroke System (OSS) was conducted. Data were collected through focus groups and in-depth interviews. Transcripts were coded and analysed using inductive thematic analysis. SETTING: Regional Stroke System, Ontario, Canada. PARTICIPANTS: OSS stakeholders including medical directors, executives, programme directors, education coordinators, rehabilitation and community and long-term care specialists, primary care leaders and healthcare professionals. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Data collection explored perceptions of the need for caregiver support programmes and factors that may affect their implementation. RESULTS: Four focus groups (n=43) and 29 interviews were completed. Analyses identified themes related to (1) evidence that a caregiver programme will improve health and health system outcomes, (2) personnel requirements, (3) barriers associated with current billing and referral processes and (4) integration with current practice and existing workflow processes. CONCLUSIONS: Implementation strategies to adopt caregiver programmes into clinical practice should incorporate evidence and consider personnel and existing workflow processes.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".