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Record W4400615458 · doi:10.22374/cjmrp.v20i1.42

Understanding the Limitations of Maternity Cost Studies: Why Context Matters

2024· article· en· W4400615458 on OpenAlexfundaboutno aff
Kellie Thiessen, Julia Rovena Witt, Alexander Peden, M. Brockman, Nathan Nickel, Margaret Morris, Kristine Robinson, Ivy Lynn Bourgeault, Shelley Derksen

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersCanadian Child Health Clinician Scientist ProgramResearch Manitoba
KeywordsContext (archaeology)PsychologyComputer scienceData scienceHistory

Abstract

fetched live from OpenAlex

Background: Limited, publicly available evidence exists to inform maternity care workforce planning in Manitoba as well as elsewhere in Canada. This manuscript offers a discussion about how context is critical when considering how cost, efficiency, and efficacy data are used.Methods: Our cost analysis of maternity care in Manitoba, Canada, focused exclusively on women with low- risk pregnancies from combined dates (2004/05 to 2008/09 and 2009/10 to 2012/13). Results: Although our cost analysis found that maternity care provided by family physicians had the lowest overall expected cost, and that highest effectiveness, measured by avoided neonatal intensive care unit (NICU) admissions, midwives had the lowest hospital costs and similar cost-effectiveness to other provider types. Interpretation: The context of how different maternity care professions are integrated into the system has a substantial impact on the assessment of overall cost. Caution must be used in interpreting these findings from significantly different models of care. The roles of providers are rarely articulated in cost study analyses to capture the breadth of services beyond “in-patient” costs. 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)0.010
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.844
GPT teacher head0.607
Teacher spread0.237 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes2
Has abstractyes

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