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Record W2183793421

Rural maternity care services under stress: the experiences of providers.

2007· article· en· W2183793421 on OpenAlexaff
Stefan Grzybowski, Jude Kornelsen, Elizabeth Cooper

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaternity careNursingContext (archaeology)Diversity (politics)Qualitative researchFocus groupRural areaEthnic groupHealth careGrounded theoryMedicineBusinessEconomic growthSociologyGeographyMarketing
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Between 2000 and 2004, 17 small rural maternity care services in British Columbia (BC) closed or were placed under moratoria. This paper explores the experiences of care providers in 4 rural BC communities that have lost or are at risk of losing their local maternity services. METHODS: We conducted qualitative, semistructured interviews and focus groups with 27 health care providers (doctors and nurses) and 3 administrators. The analysis used modified grounded theory. We chose 4 rural communities to include a diversity of characteristics, including community size, geography, distance to the nearest hospital capable of performing cesarean section, and cultural and ethnic subpopulations. RESULTS: Care providers identified significant stressors related to the provision of maternity care services, including the development and maintenance of competency in the context of decreasing birth volume, the safety of local maternity care without cesarean section and the desire to balance women's needs with the realities of rural practice. CONCLUSIONS: Maternity care providers in small rural communities are experiencing stress due in part to the absence of evidence-based policy and planning for rural maternity care services. This stress may contribute to challenges in the retention of rural maternity care providers, thus risking the future of small rural maternity services.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.306
Teacher spread0.279 · 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

Citations37
Published2007
Admission routes1
Has abstractyes

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