MétaCan
Menu
Back to cohort
Record W7053606488

Washable absorbent continence products, usability and acceptability: Project report from a three-country study in India, Papua New Guinea and Romania

2024· other· en· W7053606488 on OpenAlexaboutno aff

Bibliographic record

VenueePrints Soton (University of Southampton) · 2024
Typeother
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityNew guineaUrinary incontinenceHealth careHealth servicesQuality (philosophy)Quality of life (healthcare)Quarter (Canadian coin)Public health
DOInot available

Abstract

fetched live from OpenAlex

Introduction Incontinence (the involuntary loss of urine or faeces) is a global health and social care challenge. For many of the hundreds of millions of people living with daily incontinence globally, treatment is not available or not effective (1). For these people, the reliable containment of urine or faeces that is involuntarily lost from the body is essential to health and quality of life. The most commonly used containment devices are absorbent products (either disposable or washable), but many people need to improvise, using items such as old clothing. Only around a quarter of people requiring continence products have access to them (2). At the moment, specifically designed washable absorbent products (WAPs) are not widely used and are not easily available in most settings despite being an effective option for many. To understand more about the use of WAPs and the potential for wide-scale adoption, this research study examined the perceptions of people receiving WAPs and local service providers on the usability and acceptability of washable products, in three countries: India, Papua New Guinea and Romania.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.251
Teacher spread0.236 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueePrints Soton (University of Southampton)Same topicPeptidase Inhibition and AnalysisFrench-language works237,207