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Record W2521419588 · doi:10.1080/09638237.2017.1340619

Cost-effectiveness analysis of single-session walk-in counselling

2017· article· en· W2521419588 on OpenAlexaff
Ramesh Lamsal, Carol Stalker, Cheryl-Anne Cait, Manuel Riemer, Susan Horton

Bibliographic record

VenueJournal of Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsCentre for International Governance InnovationUniversity of WaterlooWilfrid Laurier UniversityUniversity of Calgary
Fundersnot available
KeywordsSession (web analytics)Walk-inPsychologyCost-effectiveness analysisPsychotherapistCost effectivenessMedicineComputer scienceRisk analysis (engineering)Alternative medicineWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: An increasing number of family service agencies and community-based mental health service providers are implementing a single-session walk-in counselling (SSWIC) as an alternative to traditional counselling. However, few economic evaluations have been undertaken. AIMS: To conduct a cost-effectiveness analysis of two models of service delivery, SSWIC compared to being waitlisted for traditional counselling. METHODS: A quasi-experimental design was employed. Data were collected from two community-based Family Service Agencies, one using SSWIC and one using traditional counselling. Participants were assessed at baseline and four weeks after the baseline. Cost-effectiveness was estimated from the societal and payer's perspective. RESULTS: The societal and payer's costs for SSWIC were higher than for those waiting for traditional counselling, and health outcomes were better. SSWIC is not cost-effective compared to being on the waitlist for traditional counselling (or, for a few patients, having received counselling, but after a wait of several weeks). CONCLUSIONS: SSWIC has the potential to reduce the pressure on the mental health care system by reducing emergency visits and wait lists for ongoing mental health services and eliminating costly-no shows at counselling appointments. Long-term studies involving multiple walk-in counselling services and comparison services are needed to support the findings of this study.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.139
GPT teacher head0.488
Teacher spread0.349 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2017
Admission routes1
Has abstractyes

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