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Record W2792102120 · doi:10.1080/15381501.2017.1407731

Front-line service providers’ appraisal of Saskatchewan’s HIV services and strategy: A qualitative study

2018· article· en· W2792102120 on OpenAlexaffabout
Maleeha Sami, Sithokozile Maposa, Heather Exner-Pirot, June Anonson

Bibliographic record

VenueJournal of HIV/AIDS & Social Services · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPrince Albert Grand CouncilUniversity of Saskatchewan
Fundersnot available
KeywordsFront lineQualitative researchHuman immunodeficiency virus (HIV)Service providerStigma (botany)Mental healthChristian ministryNursingHealth careService (business)MedicinePublic relationsBusinessSociologyPolitical scienceFamily medicinePsychiatryMarketing

Abstract

fetched live from OpenAlex

This qualitative study examined front-line health care providers’ understandings of the Saskatchewan Ministry of Health’s 2010–2014 HIV strategy, their capacity building needs, and perspectives on how well they were implementing HIV services. Providers’ experiences of engaging people living with HIV, community leaders, and communities affected by the epidemic reveal a need to strengthen interprofessional networks. Our findings also indicate the need for HIV services to respond to coexisting challenges, including substance use, violence, and mental health care for people living with HIV. Programs in rural northern Saskatchewan should also address homelessness, disparities in access to care, and HIV stigma issues.

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)0.009
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.541

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0140.008
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0020.003
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 head0.032
GPT teacher head0.394
Teacher spread0.362 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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