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Record W2884190577 · doi:10.3917/spub.180.0141

Motivations des agents obstétricaux qui décident d’exercer en milieu rural au Niger

2018· article· fr· W2884190577 on OpenAlexaff
Loubna Belaid, Mahaman Moha, Christian Dagenais, Valéry Ridde

Bibliographic record

VenueSanté Publique · 2018
Typearticle
Languagefr
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsThematic analysisFormative assessmentWork (physics)NursingPosition (finance)Rural areaRural healthPublic healthPsychologyPublic relationsSociologyPolitical scienceMedicineQualitative researchBusinessPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this study was to determine the individual motivations influencing health professionals' decisions to work in rural areas. This study was conducted in three of the six districts of the Tillabery region in Niger (Tillabéry, Téra and Ouallam). METHODS: We conducted 102 in-depth interviews with health professionals (physicians, nurses and midwives), which were analysed according to thematic analysis with a mixed approach (inductive and deductive). RESULTS: Multiple individual motivations influence the choice to work in rural areas: the health professional's rural origin, the low cost of living, development of the professional career (to acquire a position of responsibility and to gain experience, working in the public health system) and social relations (superiors and communities). CONCLUSION: This study highlighted the complexity of individual motivation, which depends on a multitude of factors and is expressed differently according to individual trajectories. Improving access to public health service status, and a position of responsibility providing support to health personnel by district managers through positive and formative supervision could be initiatives to support the retention of health personnel in rural areas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.017
GPT teacher head0.315
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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