Physician and Nurse Perspectives of an Interprofessional and Integrated Primary Care-Based Program for Seniors
Bibliographic record
Abstract
Background: In Canada, primary care practitioners provide the majority of care for elderly patients. Increasing volume and complexity of care compounded by a shortage of specialized geriatric services has lead to problems of fragmented, inefficient,and often ineffective service for this population. Integrated models that bridge primary and secondary care have emerged as a major theme in health reform to address such challenges for care of the elderly. Although primary care practitioners are important stakeholders necessary for successful uptake and sustainability of such integrated models, this perspective has been largely unexplored. Methods and Findings: We used a qualitative thematic approach to bring forward front-line perspectives of nurses and physicians who referred their patients to a newly developed integrated, multidisciplinary program for seniors that was introduced into their primary care clinic. Referrers experienced improved care processes, improved quality of care, as well as an enhanced experience when managing their elderly patients. Unclear assignment of roles and responsibilities created confusion for referring practitioners and their patients.Conclusions: Understanding benefits, limitations, and changes to front-line practitioner experience provides insight into important factors contributing to buy-in and sustainability of integrated programming for the elderly in this setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".