Conceptual framework: factors enabling collaborative healthcare and social services integration
Bibliographic record
Abstract
Purpose A conceptual framework for collaboratively based integrated health and social care (IHSC) integration is proposed to aid in understanding how to accomplish IHSC. Design/methodology/approach This model is based on extant literature of successfully IHSC initiatives. Findings The model aims to identify enabling integration factors that support collaborative integration efforts between healthcare and social services organizations. These factors include shared goals and vision, culture, leadership, team-based care, information sharing and communications, performance measurement and accountability agreements, and dedicated resources and financing. It also identifies factors that act as external influencers that can support or hinder integration efforts among collaborating organizations. These factors are geographic setting, funding models, governance structures, and public policies. These factors are intended to ensure that a realist lens is applied when trying to understand and explain IHSC. Originality/value This model is intended to provide a framework to support research, policy and implementation efforts.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".