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Record W3209441537 · doi:10.21203/rs.3.rs-961642/v1

Determinants of Evidence Use by Frontline Maternal, Newborn and Child Health Staff in Selected Health Facilities in Ghana

2021· preprint· en· W3209441537 on OpenAlexfundno aff
Gordon Abekah‐Nkrumah, Doris Ottie-Boakye, Johnson Ermel, Issiaka Sombié

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsChild healthEnvironmental healthMaternal healthMedicineNursingFamily medicinePsychologyHealth servicesPopulation

Abstract

fetched live from OpenAlex

Abstract Background The current paper examines the level of use of evidence and factors affecting the use of evidence by frontline maternal, newborn and child health (MNCH) and reproductive and child health (RCH) staff in practice decisions in selected health facilities in Ghana. Methods Data was collected from 509 respondents drawn from 44 health facilities in three regions in Ghana. Means were used to examine the level of use of evidence, whiles cross-tabulations and Partial least Squares-based regression were used to examine factors that influence the use of evidence in practice decisions by frontline MNCH/RCH staff. Conclusion We argue that any effort to improve the use of evidence by frontline MNCH/RC staff in practice decisions should focus on improving attitudes and knowledge of staff as well as challenges related to the structure of the organisation. Given however that the score for attitude was relatively high, emphases should be on knowledge and organizational structure in particular, which had the lowest score even though it has the single most important effect on the use of evidence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.124
GPT teacher head0.436
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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