Primary care physicians' perspectives on facilitating older patients' access to community support services: Qualitative case study.
Bibliographic record
Abstract
OBJECTIVE: To understand how family physicians facilitate older patients' access to community support services (CSSs) and to identify similarities and differences across primary health care (PHC) models. DESIGN: Qualitative, multiple-case study design using semistructured interviews. SETTING: Four models of PHC delivery, specifically 2 family health teams (FHTs), 4 non-FHTs family health organizations, 4 fee-for-service practices, and 2 community health centres in urban Ontario. PARTICIPANTS: Purposeful sampling of 23 family physicians in solo and small and large group practices within the 4 models of PHC. METHODS: A multiple-case study approach was used. Semistructured interviews were conducted and data were analyzed using within- and cross-case analysis. Case study tactics to ensure study rigour included memos and an audit trail, investigator triangulation, and the use of multiple, rather than single, case studies. MAIN FINDINGS: Three main themes were identified: consulting and communicating with the health care team to create linkages; linking patients and families to CSSs; and relying on out-of-date resources and ineffective search strategies for information on CSSs. All participants worked with their team members; however, those in FHTs and community health centres generally had a broader range of health care providers available to assist them. Physicians relied on home-care case managers to help make linkages to CSSs. Physicians recommended the development of an easily searchable, online database containing available CSSs. CONCLUSION: This study shows the importance of interprofessional teamwork in primary care settings to facilitate linkages of older patients to CSSs. The study also provides insight into the strategies physicians use to link older persons to CSSs and their recommendations for change. This understanding can be used to develop resources and approaches to better support physicians in making appropriate linkages to CSSs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".