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Record W2442290867 · doi:10.1002/jcop.21787

SUSTAINING MOTIVATION AMONG COMMUNITY HEALTH WORKERS IN AIDS CARE IN KWAZULU‐NATAL, SOUTH AFRICA: CHALLENGES AND PROSPECTS

2016· article· en· W2442290867 on OpenAlexaff
Wenche Dageid, Olagoke Akintola, Therese Sæberg

Bibliographic record

VenueJournal of Community Psychology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsMcMaster UniversityNipissing University
Fundersnot available
KeywordsRemunerationEmpathyStigma (botany)Health careNursingAltruism (biology)PsychologyPublic relationsMedicineEconomic growthPolitical scienceSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

A substantial part of community‐based AIDS care in South Africa is currently undertaken by poor, female volunteer community health workers (CHWs). Retention of volunteers over time is challenging, however. In this study, 12 female AIDS care volunteers in KwaZulu‐Natal, South Africa, were interviewed about their motivations for becoming volunteers, perceived challenges in care work, and reasons for sustained volunteering. All women reported altruism and empathy as their main motivation for volunteering. Motivations for sustained volunteering included supportive networks, hopes of future employment in the formal health care system, personal growth, and appreciation from patients and community members. Despite reporting several challenges, all women were motivated to continue volunteering. To encourage retention, policy makers should pay attention to personal and professional rewards gained from volunteering, create career paths and clarify CHWs' roles and rights in the health care sector, and provide various coordination and support measures, including remuneration and stigma reduction.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.379
Teacher spread0.280 · 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 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

Citations26
Published2016
Admission routes1
Has abstractyes

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