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Record W3203450505 · doi:10.1177/10497323211037636

Culture’s Place in Quality of Care in a Resource-Constrained Health System: Comparison Between Three Malawi Districts

2021· article· en· W3203450505 on OpenAlexafffund
Patrick B. Patterson, Zubia Mumtaz, Ellen Chirwa, Janet Mambulasa, Fannie Kachale, Josephat Nyagero

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchGlobal Affairs CanadaInternational Development Research Centre
KeywordsOrganizational cultureQuality (philosophy)EthnographyHealth careSociologyTransactional leadershipPublic relationsResource (disambiguation)NursingPsychologyKnowledge managementMedicinePolitical scienceComputer scienceEconomic growthEconomicsEpistemology

Abstract

fetched live from OpenAlex

Public health scholars describe "culture of quality" in terms of desired values, attitudes, and practices, but this literature rarely includes explicitly stated theories of culture formation. In this article, we apply Fredrik Barth's transactional model to demonstrate how taking a theory-centered approach can help to identify what would be necessary to foster "cultures of quality" outlined in the public health literature. We draw on data from a study of the Republic of Malawi's Performance and Quality Improvement for Reproductive Health initiative. These data were generated in 2017-2018 through a 6-month organizational ethnography in three facilities selected to represent a range of districts with differing social and economic contexts. Our analysis revealed facility-level organizational cultures in which staff valued providing care, but responded to structural constraints by normalizing divergence from quality-of-care protocols. These findings indicate that sustaining a quality-oriented organizational culture requires addressing underlying conditions that generate routine experiences and practices.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.341
GPT teacher head0.578
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2021
Admission routes2
Has abstractyes

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