Community-based primary health care for older adults: a qualitative study of the perceptions of clients, caregivers and health care providers
Bibliographic record
Abstract
BACKGROUND: Older persons are often poorly served by existing models of community-based primary health care (CBPHC). We sought input from clients, informal caregivers, and health care providers on recommendations for system improvements. METHODS: Focus group interviews were held with clients, informal caregivers, and health care providers in mid-sized urban and rural communities in Ontario. Data were analyzed using a combination of directed and emergent coding. Results were shared with participants during a series of feedback sessions. RESULTS: An extensive list of barriers, facilitators, and recommended health system improvements was generated. Barriers included poor system integration and limited access to services. Identified facilitators were person and family-focused care, self-management resources, and successful collaborative practice. Recommended system improvements included expanding and integrating care teams, supports for system navigation, and development of standardized information systems and care pathways. CONCLUSIONS: Older adults still experience frustrating obstacles when trying to access CBPHC. Identified barriers and facilitators of improved system integration aligned well with current literature and Wagner's Chronic Care Model. Additional work is needed to implement the recommended improvements and to discern their impact on patient and system outcomes.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".