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Record W2767557097 · doi:10.1037/ser0000158

Meeting the mental health needs of today’s college student: Reinventing services through Stepped Care 2.0.

2017· article· en· W2767557097 on OpenAlexaff
Peter Cornish, Gillian Berry, Sherry A. Benton, Patrícia Barros-Gomes, Dawn Johnson, Rebecca Ginsburg, Beth Whelan, Emily Fawcett, Vera Romano

Bibliographic record

VenuePsychological Services · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPsycINFOAutonomyMental healthHealth careService (business)Medical educationMental health carePsychologyService delivery frameworkNursingComputer scienceMEDLINEMedicinePolitical scienceBusinessPsychiatryMarketing

Abstract

fetched live from OpenAlex

A new stepped care model developed in North America reimagines the original United Kingdom model for the modern university campus environment. It integrates a range of established and emerging online mental health programs systematically along dimensions of treatment intensity and associated student autonomy. Program intensity can be either stepped up or down depending on level of client need. Because monitoring is configured to give both provider and client feedback on progress, the model empowers clients to participate actively in care options, decisions, and delivery. Not only is stepped care designed to be more efficient than traditional counseling services, early observations suggest it improves outcomes and access, including the elimination of service waitlists. This paper describes the new model in detail and outlines implementation experiences at 3 North American universities. While the experiences implementing the model have been positive, there is a need for development of technology that would facilitate more thorough evaluation. (PsycINFO Database Record

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.198
GPT teacher head0.496
Teacher spread0.298 · 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 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

Citations131
Published2017
Admission routes1
Has abstractyes

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