Enhancing Knowledge and InterProfessional care for Heart Failure (EKWIP-HF) in long-term care: a pilot study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) affects 20% of long-term care (LTC) residents and is associated with significant morbidity, acute care visits, and mortality. Barriers to HF management are staff knowledge gaps and ineffective interprofessional (IP) communication. This pilot study assessed the acceptability, feasibility, and impact of an intervention to (1) improve HF knowledge; (2) improve IP communication; and (3) integrate improved knowledge and communication processes into work routines. METHODS: The intervention provides multimodal IP education about HF in LTC, including specialist-supported bedside teaching. It was piloted on single units in two facilities. A mixed-methods repeated-measures approach was used to collect qualitative and quantitative process and outcome data at baseline and 6 months post-intervention. RESULTS: Results were similar at both sites. Participants developed optimized IP communication to promote HF care. Results indicate a perceived increase in staff confidence and self-efficacy, strengthened assessment and clinical proficiency skills, and more effective IP collaboration. Staff deemed the intervention useful and feasible. CONCLUSIONS: This pilot study suggests that a novel intervention in which HF-specific knowledge is applied by LTC staff to improve IP collaboration in their own work place is acceptable and feasible and has a favourable preliminary impact on staff knowledge and IP communication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".