Healthcare professionals’ perspectives on barriers and facilitators to implementing a warning signs intervention for older rural-dwelling medical patients at risk for hospital readmission
Bibliographic record
Abstract
INTRODUCTION: Prior research has identified that older rural patients and their families view preparation for detecting and responding to worsening health after a hospital stay as their most pressing unmet need, and perceive an evidence-based warning signs intervention that prepares them to do so as highly likely to meet this need. Yet, little is known about healthcare professionals' perspectives about potential barriers and facilitators to implementing warning signs interventions, especially in rural communities. AIM: This study aimed to identify potential barriers and facilitators to healthcare professionals' provision of a warning signs intervention in rural communities. MATERIALS AND METHODS: In this qualitative descriptive study, we examined healthcare professionals' perspectives on potential barriers and facilitators to providing a warning signs intervention. A purposive, criterion-based sample of healthcare professionals, stratified by professional designation (three strata - nurses, physicians, and allied healthcare professionals) who provide health care to rural dwellers in Ontario, Canada participated in semi-structured telephone focus-group discussions or 1:1 interviews on barriers and facilitators to delivering the intervention. Data were analyzed using conventional qualitative content analysis. RESULTS: Twenty-seven healthcare professionals participated in focus groups and 15 in 1:1 interviews for a total of 42 healthcare professionals. Analysis by healthcare professional stratum revealed nine categories of barriers and facilitators: material resources; human resources; healthcare professional communication; healthcare professional knowledge and skill; healthcare professional buy-in; context of rural practice; patient- and family-specific characteristics; risks and liabilities; and timing of intervention delivery. Seven of these categories converged across healthcare professional strata. However, the reasons why different healthcare professional strata perceived the categories as important, and the ways in which they saw them functioning as barriers and facilitators, varied. Our findings shed light on barriers and facilitators that should be considered to ensure successful implementation of the intervention in rural communities. DISCUSSION: This study adds to the limited research on rural healthcare professionals' perspectives on barriers and facilitators to delivering a warning signs intervention.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".