Heart failure management insights from primary care physicians and allied health care providers in Southwestern Ontario
Bibliographic record
Abstract
BACKGROUND: It remains to be determined whether collaborative strategies to improve and sustain overall health in patients with heart failure (HF) are currently being adopted by health care professionals. We surveyed primary care physicians, nurses and allied health care professionals in Southwestern Ontario regarding how they currently manage HF patients and how they perceive limitations, barriers and challenges in achieving optimal management in these patients. METHODS: We developed an online survey based on field expertise and a review of pertinent literature in HF management. We analyzed quantitative data collected via an online questionnaire powered by Qualtrics®. The survey included 87 items, including multiple choice and free text questions. We collected participant demographic and educational background, and information relating to general clinical practice and specific to HF management. The survey was 25 min long and was administered in October and November of 2018. RESULTS: We included 118 health care professionals from network lists of affiliated physicians and clinics of the department of Family Medicine at Western University; 88.1% (n = 104) were physicians while 11.9% (n = 14) were identified as other health care professionals. Two-thirds of our respondents were females (n = 72) and nearly one-third were males (n = 38). The survey included mostly family physicians (n = 74) and family medicine residents (n = 25). Most respondents indicated co-managing their HF patients with other health care professionals, including cardiologists and internists. The vast majority of respondents reported preferring to manage their HF patients as part of a team rather than alone. As well, the majority respondents (n = 47) indicated being satisfied with the way they currently manage their HF patients; however, some indicated that practice set up and communication resources, followed by experience and education relating to HF guidelines, current drug therapy and medical management were important barriers to optimal management of HF patients. CONCLUSIONS: Most respondents indicated HF management was satisfactory, however, a minority did identify some areas for improvement (communication systems, work more collaborative as a team, education resources and access to specialists). Future research should consider these factors in developing strategies to enhance primary care involvement in co-management of HF patients, within collaborative and multidisciplinary systems of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".