'Working at the margins' or 'leading from behind'?: a Canadian study of hospital-community collaboration
Bibliographic record
Abstract
Collaboration between hospitals and community organisations has been promoted over the past 20 years by various levels of government, hospital associations, health promotion advocates, and others at the state/province, national and international levels as a way to improve the 'efficiency of the system', reduce duplication, enhance effectiveness and service coordination, improve continuity of care, and enhance community capacity to address complex issues. Nevertheless, and despite a growing literature on interagency collaboration, systematic documentation and empirical analysis of hospital-community collaboration (HCC) is almost completely lacking in the literature, particularly as regards collaborations that address the determinants of health beyond the hospital walls. In this paper, we describe the methodology and key findings from a research study of HCC. The Hospital Involvement in Community Action (HICA) study undertook detailed qualitative case studies (in four urban, suburban, rural and northern locations) and a telephone survey (of 139 community organisations in a large urban centre) in order to learn about the range of collaborations and working relationships that exist between hospitals and community agencies in the province of Ontario (Canada), and the factors that influenced (enabled and/or hindered) HCC. Particular attention was paid to barriers and enablers at three nested levels of context (policy, hospital and community) and, drawing primarily on the qualitative case studies, it is this aspect that is the focus of this paper. That such collaborations continue to be widespread despite a generally unfavourable policy environment and hospital institutional culture that poses significant barriers, suggests that the extent to which HCC flourishes (or exists at all) crucially depends on the presence and ongoing enthusiasm/commitment of one or more 'champions' within the hospital, and the commitment of both parties to overcome the marked cultural differences between hospital and community. We conclude with a discussion of implications for policy and practice.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.060 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".