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Record W2024263483 · doi:10.1080/15381500802307518

Managing Funding Constraints in Frontline HIV/AIDS Social Services in Canada

2008· article· en· W2024263483 on OpenAlexaffabout
Roy Cain, Sarah Todd

Bibliographic record

VenueJournal of HIV/AIDS & Social Services · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCarleton UniversityMcMaster University
Fundersnot available
KeywordsSocial workBureaucracyGovernment (linguistics)Public relationsService delivery frameworkHuman immunodeficiency virus (HIV)Work (physics)Context (archaeology)BusinessService (business)Economic growthPolitical scienceMarketingMedicineEconomicsPoliticsFamily medicine

Abstract

fetched live from OpenAlex

ABSTRACT The need for HIV/AIDS social services continues to grow; workers confront increasingly complex client needs, and changes to government funding policies present new challenges to frontline social workers. Drawing on a qualitative study involving 59 social service practitioners in Ontario, Canada, this paper explores how frontline workers experience policy changes that restrict government funding. Respondents report that many of their clients are focused on immediate needs, such as food or shelter, rather than their HIV infection. They describe how funding cuts can introduce uncertainty into their work, and they note that their work now feels more bureaucratic and less caring. More organizational work is required to access resources and workers often find themselves playing a gate-keeping role. The paper describes how workers try to minimize the negative policy changes they perceive, but some of their ways of managing increased demand can actually end up increasing barriers to clients. Findings suggest that changing policies can undermine efforts to involve people with HIV in service delivery and to advocate for changing the social context of the epidemic.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.341
Teacher spread0.309 · 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 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
Published2008
Admission routes2
Has abstractyes

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