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Record W2089424176 · doi:10.1353/hpu.2010.0662

Determining Needs and Setting Priorities for HIV-Affected and HIV-Infected Persons: Northeast Ohio and San Diego

2000· article· en· W2089424176 on OpenAlexaff
Sana Loue, Marlene Faust, Daniel J. O’Shea

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2000
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsHumber River Regional Hospital
Fundersnot available
KeywordsCARE ActNeeds assessmentHuman immunodeficiency virus (HIV)Medical prescriptionMedicinePaymentGerontologyHealth careEnvironmental healthBusinessFamily medicineNursingEconomic growthPolitical scienceFinance

Abstract

fetched live from OpenAlex

The Ryan White Comprehensive AIDS Resources Emergency (CARE) Act of 1991 requires that communities receiving Title I funding engage in a needs assessment and priority process to guide the allocation of those funds to various services within the local community. This paper reports on the process and results of the needs assessments in northeast Ohio and San Diego County for 1996-1997 and 1998. Data from northeast Ohio's 1998 needs assessment indicated significant differences between whites and nonwhites in the utilization of HIV specialist care, HIV-related prescription medications such as antiretrovirals, and health insurance. A need for additional dental care, complementary therapies, housing, and assistance with utility payments was found in both geographic areas. Consumer participation in San Diego's health department-based needs assessment process was more extensive than in northeast Ohio's academic-based approach but was also related to increased community-borne expense.

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)0.002
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 score0.161
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.041
GPT teacher head0.361
Teacher spread0.320 · 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

Citations6
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Health Care for the Poor and UnderservedSame topicHomelessness and Social IssuesFrench-language works237,207