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Record W4386021932 · doi:10.29392/001c.84086

Self-care interventions for advancing sexual and reproductive health and rights – implementation considerations

2023· article· en· W4386021932 on OpenAlexaff
Manjulaa Narasimhan, Carmen H. Logie, James Hargreaves, Wendy Janssens, Mandip Aujla, Petrus S. Steyn, Erica van der Sijpt, Anita Hardon

Bibliographic record

VenueJournal of Global Health Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionHealth careReproductive healthAgency (philosophy)NursingMedicineNormativePsychologyEnvironmental healthPolitical sciencePopulationSociology

Abstract

fetched live from OpenAlex

Self-care refers to the ability of people to promote their own health, prevent disease, maintain health, and cope with illness and disability, with or without the support of a health or care worker. Self-care interventions are tools that support self-care as additional options to facility-based care. Recognizing laypersons as active agents in their own health care, the World Health Organization (WHO)’s global normative guideline on self-care interventions recommends people-centred, holistic approaches to health and well-being for sexual and reproductive health and rights. Examples of such interventions include pregnancy self-testing, self-monitoring of blood glucose and/or blood pressure during pregnancy and self-administration of injectable contraception. Building on previous studies and aligning with the WHO classification for self-care, we discuss nine key implementation considerations: agency, information, availability, utilization, social support, accessibility, acceptability, affordability, and quality. The implementation considerations form the foundation of a model implementation framework that was developed using an ecological health systems approach to support sustainable changes in health care delivery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.083
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.444
Teacher spread0.412 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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