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Record W4226091843 · doi:10.1111/hex.13491

Network‐building by community actors to develop capacities for coproduction of health services following reforms: A case study

2022· article· en· W4226091843 on OpenAlexaff
Susan Usher, Jean‐Louis Denis

Bibliographic record

VenueHealth Expectations · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité de MontréalÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsCoproductionService providerPublic relationsSocial network analysisContext (archaeology)LegitimacySocial network (sociolinguistics)Knowledge managementBusinessService (business)SociologyPolitical scienceMarketingPoliticsComputer scienceSocial capital

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0150.009
Scholarly communication0.0050.006
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.132
GPT teacher head0.468
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations7
Published2022
Admission routes1
Has abstractyes

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