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Record W2756663021 · doi:10.1093/intqhc/mzx125.14

ISQUA17-1957THE VALUE OF ARTS BASED METHODS TO EMPOWER PREGNANT AND POSTNATAL WOMEN TO SHARE THEIR SAFETY CONCERNS ABOUT SERIOUS ILLNESS

2017· article· en· W2756663021 on OpenAlexaff
Nicola Mackintosh, James M. Harris, Chris Collison, Jane Sandall

Bibliographic record

VenueInternational Journal for Quality in Health Care · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsValue (mathematics)The artsMedicineNursingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Emergencies during pregnancy and birth, while unusual, can develop rapidly and unexpectedly, with catastrophic consequences. Women's tacit knowledge of changes in their condition is an important resource to aid early detection and diagnosis, but women can worry about the legitimacy of their concerns, and struggle at times to express and get these taken seriously by staff. While there is a plethora of health information about maternity complications, it tends to be condition specific with an emphasis on technical rationality; lists of early warning signs may not map to women's experiences. This UK project was funded by the Cultural Institute at King's College London and involved a collaboration between health researchers and the cultural sector. The project aimed to co-design a film animation for pregnant and postnatal women to enable them to speak up and secure help for serious safety concerns. We planned to test as proof of concept its value with women and staff, and the feasibility of delivering the intervention as intended (engagement, acceptability and uptake).

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.093
GPT teacher head0.533
Teacher spread0.440 · 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 designQualitative
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
Published2017
Admission routes1
Has abstractyes

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