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Record W4414609821 · doi:10.22374/cjmrp.v20i2.39

An Early Feasibility Study of Midwifery Services in a Vulnerable Population

2024· article· en· W4414609821 on OpenAlexaboutno aff
Mahnoush Rostami, Paola Charland, Ameera Memon, Zoe Hsu, Esther Suter

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupPrenatal carePopulationHealth servicesPublic healthHealth carePregnancy

Abstract

fetched live from OpenAlex

Objectives: Canadian women and newborns are usually healthy due to the availability of prenatal care, postnatal care, and the presence of a skilled health professional. However, social determinants of health can have a significant impact on women’s ability to access high-quality care, particularly during pregnancy. We partnered with Aspen, a not-for-profit social service organization in Calgary, to explore the feasibility of implementing midwifery services for a vulnerable population. Methods: We conducted interviews with Aspen clients, Calgary registered midwives, and focus groups with Aspen staff to understand their perceptions of midwifery services, including benefits and potential barriers to their implementation. We used administrative data to develop a demographic profile of Aspen clients. Results: Our results suggest that midwives would be acceptable birth providers, but this further depends on women’s culture and their previous pregnancy experience. The study highlighted key aspects that should be considered to successfully implement midwifery services for the vulnerable population, including public awareness about midwifery services, access to an interprofessional team, and allocation of additional funding to practicing midwives. Conclusion: Midwifery care would be an acceptable and perhaps more-appropriate maternity care model for vulnerable populations. This article has been peer reviewed.

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)0.005
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 score0.042
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.112
GPT teacher head0.458
Teacher spread0.346 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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