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Record W4412708034 · doi:10.22605/rrh9205

Framing ârural health equityâ and implications for governance: thematic analysis of 51 expert narratives from a global webinar series

2025· article· en· W4412708034 on OpenAlexfundno aff
Theadora Swift Koller, Bruce Chater

Bibliographic record

VenueRural and Remote Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
FundersGovernment of CanadaWorld Health Organization
KeywordsNarrativePublic relationsHealth equityFraming (construction)Thematic analysisEquity (law)SociologyHealth careCorporate governanceRural healthReflexivityPolitical scienceQualitative researchSocial scienceManagementEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

INTRODUCTION: To respond to persisting gaps in health service coverage and health outcomes impacting rural populations globally, governance for rural health equity requires enhanced focus by policymakers, researchers and practitioners. During 2021-22, 51 experts from around the world contributed (as speakers, co-chairs and discussants) to an eight-part webinar series on rural health equity convened by WHO and Rural WONCA, with inputs from partners including the OECD and agencies in the UN Inequalities Task Team subgroup on rural inequalities. The aim of the webinar series was to share technical/operational know-how and lessons learnt for addressing rural health inequities. METHODS: A thematic analysis of all webinar expert narratives was completed by the authors during 2022-23, with the purpose of using the data to conceptually feed into multiple WHO technical and capacity-building products. Following transcription, this entailed familiarization with the data and reflexivity (including on the framework used to inform the series and the researchers' roles), generation of codes, combining codes in categories and themes, further analysis and reporting (alongside amendment of the original framework). The research question was 'What do the 51 expert narratives from the WHO Rural Health Equity eight-part webinar series convey about the framing of rural health equity and related governance approaches?' RESULTS: Expert narratives provided evidence suggesting that the framing of rural health equity needs to account for primary health care-oriented health systems strengthening issues in a way that highlights their indivisible, interrelated and synergistic nature, taking a system-wide approach. Expert narratives pointed to the health sector having an active role in rural development policy, as a platform to leverage action for rural health equity through working across sectors to address social and environmental determinants of health. In framing the equity dimension of rural health equity, there was a clear acknowledgement in expert narratives that the concept comprises inequitable differences both between urban and rural areas and within rural areas. Narratives underlined that a historical lens is required to understand the drivers of rural health inequities, as well as formulate or improve - through participatory approaches - the strategies to overcome them. The narratives shed light on governance issues such as inter- and intrasectorial policy and programming coherence, effective rural-proofing mechanisms, evidence-based decision-making drawing from strengthened equity-oriented information systems, ground-up participatory decision-making approaches, rights-based governance (including for self-determination), and greater accountability for redressing socio-spatial inequities and optimizing rural communities' assets. Findings suggest that unlocking rural health inequities will require the further study of government commitments, governance mechanisms, and capacities to effectively implement measures for territorially balanced development and area-based strategies for equity within and between territories. CONCLUSION: The findings have relevance for the further design of policies, programming, and monitoring and evaluation for rural health equity by national and subnational authorities, as well as for the activities of researchers, WHO, Rural WONCA and partners.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.457
Teacher spread0.420 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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