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Record W4311534379 · doi:10.1177/10497323221143889

Nigerian Health Care Providers and Diabetes Self-Management Support: Their Perspectives and Practices

2022· article· en· W4311534379 on OpenAlexaff
Sandra Iregbu, Jude Spiers, Wendy Duggleby, Bukola Salami, Kara Schick‐Makaroff

Bibliographic record

VenueQualitative Health Research · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSelf-managementNursingQualitative researchHealth careDiabetes mellitusSelf careDiabetes managementMedicinePsychologyType 2 diabetesPolitical scienceSociology

Abstract

fetched live from OpenAlex

Nigeria struggles to reframe its traditional acute-care disease approach to health care to accommodate rising needs for chronic disease care. This interpretive descriptive study explored Nigerian healthcare providers' (HCPs) perspectives, experiences, and practices related to self-management support (SMS). Observational and experiential data were gathered from 19 HCPs at two urban hospitals in Southeastern Nigeria (seven physicians, four nurses, five dietitians/nutritionists, and three health educators). There were four themes: (a) compliance-oriented medical model, (b) SMS as advice, informal counseling, and education, (c) navigating the sociocultural terrain, and (d) workarounds. Nigerian HCPs perspectives and SMS practices were characterized by attempts to foster compliance with healthcare instructions within a traditional biomedical model. Participants enhanced patient support using specific strategies to bypass structural system obstacles. These findings demonstrate the need to reevaluate the current understanding of SMS in Nigeria and its practice.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.240
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.539
Teacher spread0.360 · 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 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".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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