Collaborating with healthcare providers to understand their perspectives on a hospital-to-home warning signs intervention for rural transitional care: protocol of a multimethod descriptive study
Bibliographic record
Abstract
INTRODUCTION: This study builds on our prior research, which identified that older rural patients and families (1) view preparation for detecting and responding to worsening health conditions as their most pressing unmet transitional care (TC) need and (2) perceive an evidence-based intervention, preparing them to detect and respond to warning signs of worsening health conditions, as highly likely to meet this need. Yet, what healthcare providers need to implement a warning signs intervention in rural TC is unclear. The objectives of this study are (1) to examine healthcare providers' perspectives on the acceptability of a warning signs intervention and (2) to identify barriers and facilitators to healthcare providers' provision of the intervention in rural communities. METHODS AND ANALYSIS: This multimethod descriptive study uses a community-based, participatory research approach. We will examine healthcare providers' perspectives on a warning signs intervention. A purposive, criterion-based sample of healthcare providers stratified by professional designation (three strata: nurses, physicians and allied healthcare professionals) in two regions (Southwestern and Northeastern Ontario, Canada) will (1) rate the acceptability of the intervention and (2) participate in small (n=4-6 healthcare providers), semistructured telephone focus group discussions on barriers and facilitators to delivering the intervention in rural communities. Two to three focus groups per stratum will be held in each region for a total of 12-18 focus groups. Data will be analysed using conventional qualitative content analysis and descriptive statistics. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Office of Research Ethics at York University and the Health Sciences North Research Ethics Board. Findings will be communicated through plain language summary and policy briefs, press releases, manuscripts and conferences.
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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 | 0.090 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".