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Record W4312247656 · doi:10.32920/ihtp.v2i2.1637

Gender, time-use, and health: A scoping review

2022· review· en· W4312247656 on OpenAlexvenueno aff
Kanika Sharma, Ramila Bisht

Bibliographic record

VenueInternational Health Trends and Perspectives · 2022
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthLife course approachDiversity (politics)GerontologyThematic analysisQualitative researchPsychologyApplied psychologyMedicineSociologyDevelopmental psychologySocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Introduction: Time-use research is a useful approach to examine the health impacts of how people spend their time and the factors that influence their time. One such factor is gender. Aim: This study undertakes a scoping review to map and synthesize recent research done on the interrelationships among gender, time-use, and health. Design: Web of Science and PubMed electronic databases were searched to identify research published between 2015 and 2020. Forty-four studies that met the eligibility criteria were selected. Results: Most studies on the topic are quantitative in nature, focus on developed country contexts, and have mental health and nutrition as thematic health focus. There is diversity in the kinds of population being studied, with an increasing focus on children and adolescent populations. Conceptual findings reveal multi-directional and life-course aspects of the relationship; point that the relationship between time-use and health varies by the stage of the epidemiological and the nutrition transition; and highlight the need to study the health and well-being impacts of gendered caregiving. Conclusion: This review highlights the need to conduct qualitative studies, give attention to health outcomes such as chronic illnesses, occupational health issues, and physical pain, and increase research focus on developing country contexts where gender inequality in time-use and health is severe.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.446
GPT teacher head0.592
Teacher spread0.146 · 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 designOther design
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes1
Has abstractyes

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