Network‐building by community actors to develop capacities for coproduction of health services following reforms: A case study
Bibliographic record
Abstract
INTRODUCTION: Responsive, integrated and sustainable health systems require that communities take an active role in service design and delivery. Much of the current literature focuses on provider-led initiatives to gain community input, raising concerns about power imbalances inherent in invited forms of participation. This paper provides an alternate view, exploring how, in a period following reforms, community actors forge network alliances to (re)gain legitimacy and capacities to coproduce health services with system providers. METHODS: A longitudinal case study traced the network-building efforts over 3 years of a working group formed by citizens and community actors working with seniors, minorities, recent immigrants, youth and people with disabilities. The group came together over concerns about reforms that impacted access to health services and the ability of community groups to mediate access for vulnerable community residents. Data were collected from observation of the group's meetings and activities, documents circulated within and by the group, and semi-directed interviews. The first stage of analysis used social network mapping to reveal the network development achieved by the working group; a second traced network maturation, based on actor-network theory. RESULTS: Network mapping revealed how the working group mobilized existing links and created new links with health system actors to explore access issues. Problematization appeared as an especially important stage in network development in the context of reforms that disrupted existing collaborative relationships and introduced new structures and processes. CONCLUSION: Network-building strategies enable community actors to enhance their capacity for coproduction. A key contribution lies in the creation of 'organizational infrastructure'. PATIENT OR PUBLIC CONTRIBUTION: The lead researcher was embedded over 3 years in the activities of the community groups and community residents. Several group members provided comments on an initial draft of this paper. To preserve the anonymity of the group, their names do not appear in the acknowledgements section.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.021 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".