Coordinating care for older adults in primary care settings: understanding the current context
Bibliographic record
Abstract
BACKGROUND: It is well known that older adults are high users of the health care system. Older adults with chronic conditions receive care from multiple providers, across multiple settings, and this care is often unorganized and confusing. In 2005, Ontario established a model of inter-professional primary care (family health teams) with the aim of providing enhanced interdisciplinary primary care to patients. Primary care requires an in-depth understanding of the operations of primary care teams and their relationships with other community services. The aim of this study was to develop a deeper understanding of the current operations of two family health teams in Ontario, including their current processes for referrals, information sharing, and engagement of patients in decision-making. METHODS: Focus group and individual semi-structured interviews with health care providers were conducted. Purposeful sampling was used to ensure information was obtained from different professional perspectives. Interviews were audio-recorded and transcribed verbatim. Using NVivo 10, data were analyzed using line by line thematic analysis techniques. A cluster technique was then applied to group similar codes into themes. RESULTS: Three focus group interviews (involving 4-6 health care providers/focus group) and six individual interviews were conducted with health care providers from two primary care teams and surrounding community care organizations. Six key themes were identified: 1) challenges engaging older adults in decisions about their care; 2) who is responsible for coordinating the care? 3) fragmented information sharing between health care providers; 4) lack of standardized referral processes and follow-up; 5) identifying services in the community for older adults; and 6) caring for older adults in rural communities. CONCLUSIONS: The results of this study provide an in-depth understanding of the current context in which the primary care teams are currently operating. Improved primary care will require stronger processes of coordination, greater knowledge of and connections with other community services, and enhanced patient engagement processes. This information provides a helpful basis for implementing interventions in primary care.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".