Community Care for People with Complex Care Needs: Bridging the Gap between Health and Social Care
Bibliographic record
Abstract
INTRODUCTION: A growing number of people are living with complex care needs characterized by multimorbidity, mental health challenges and social deprivation. Required is the integration of health and social care, beyond traditional health care services to address social determinants. This study investigates key care components to support complex patients and their families in the community. METHODS: Expert panel focus groups with 24 care providers, working in health and social care sectors across Toronto, Ontario, Canada were conducted. Patient vignettes illustrating significant health and social care needs were presented to participants. The vignettes prompted discussions on i) how best to meet complex care needs in the community and ii) the barriers to delivering care to this population. RESULTS: Categories to support care needs of complex patients and their families included i) relationships as the foundation for care, ii) desired processes and structures of care, and iii) barriers and workarounds for desired care. DISCUSSION AND CONCLUSIONS: Meeting the needs of the population who require health and social care requires time to develop authentic relationships, broadening the membership of the care team, communicating across sectors, co-locating health and social care, and addressing the barriers that prevent providers from engaging in these required practices.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".