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Record W2140843148 · doi:10.3109/09638237.2012.670880

The case for single-session therapy: Does the empirical evidence support the increased prevalence of this service delivery model?

2013· review· en· W2140843148 on OpenAlexaff
Peter Hymmen, Carol Stalker, Cheryl-Anne Cait

Bibliographic record

VenueJournal of Mental Health · 2013
Typereview
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSession (web analytics)PsychologyService (business)Empirical researchMedicinePsychotherapistClinical psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: A significant increase in the number of walk-in counselling clinics offering single-session therapy (SST) prompted this review of the empirical support for the effectiveness of SST. AIMS: The article is intended to (1) increase practitioners' knowledge of the empirical support for the effectiveness of single-session counselling with client populations typically served in community-based mental health and counselling agencies and (2) identify priorities for future research on SST. METHOD: A thorough review of relevant databases was undertaken to locate published studies reporting client outcomes following SST. The focus of the review is research involving clients and presenting problems typically seen in community-based mental health and family counselling agencies. RESULTS: The findings suggest that the majority of clients attending either previously scheduled or walk-in SST find it sufficient and helpful. The studies imply that this model of service delivery leads to perceived improvement in presenting problems in general, and on specific measures of variables such as depression, anxiety, distress level and confidence in parenting skills. CONCLUSIONS: Many of the studies have methodological limitations, and future research requires increased use of standardized measures, control groups and larger and more diverse samples.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.283
GPT teacher head0.458
Teacher spread0.176 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations114
Published2013
Admission routes1
Has abstractyes

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