Barriers to the Management of Heart Failure in ON Long Term Care Homes: An Interprofessional Care Perspective
Bibliographic record
Abstract
Background: With population aging, the prevalence of heart failure (HF) is rising in long-term care (LTC) homes.Given this burden, there is an urgent need to establish effective HF management programs. Methods and Findings:To understand what barriers would need to be addressed to develop such a program, we conducted a series of consultations among various LTC staff, as well as residents and their family caregivers.This article uses data obtained from the consultations to describe the interprofessional (IP) barriers that exist among the various LTC staff roles.Consultation methods included a Delphi survey followed by focus group interviews of LTC staff, and then personal interviews with LTC residents with HF and their family caregivers.Data were interpreted using an IP care framework in which interpersonal relationships among LTC staff provide the most direct influence on collaborative resident-centred practice, within the broader context of conditions within the LTC home, which in turn are housed in the broader context of systemic determinants.Conclusion: Across all data sets, the most consistently mentioned determinant was communication between the resident and the healthcare team, between different healthcare providers, between shifts, between medical specialists, and between the long-term care home and the hospital.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".