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Record W2099880395 · doi:10.1186/1747-597x-7-4

Substance abuse treatment client experience in an employed population: results of a client survey

2012· article· en· W2099880395 on OpenAlexaboutno aff
Elizabeth L. Merrick, Sharon Reif, Deirdre Hiatt, Dominic Hodgkin, Constance M. Horgan, Grant A. Ritter

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2012
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsQuarter (Canadian coin)MedicineSubstance abusePopulationHealth careService (business)PsychiatrySubstance abuse treatmentSocial workFamily medicineNursingPsychologyBusinessEnvironmental healthMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding client perspectives on treatment is increasingly recognized as key to improving care. Yet information on the perceptions and experiences of workers with private insurance coverage who receive help for substance use conditions is relatively sparse, particularly in managed behavioral health care organization (MBHO) populations. Furthermore, the role of several factors including prior service use has not been fully explored. METHODS: Employees covered by a large MBHO who had received substance abuse services in the past year were surveyed (146 respondents completed the telephone survey and self-reported service use). RESULTS: The most common reasons for entering treatment were problems with health; home, family or friends; or work. Prior treatment users reported more reasons for entering treatment and more substance use-related work impairment. The majority of all respondents felt treatment helped a lot or some. One quarter reported getting less treatment than they felt they needed. DISCUSSION AND CONCLUSIONS: Study findings point to the need to tailor treatment for prior service users and to recognize the role of work in treatment entry and outcomes. Perceived access issues may be present even among insured clients already in treatment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.096
GPT teacher head0.390
Teacher spread0.294 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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