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Record W3103598494 · doi:10.1521/jsyt.2020.39.3.21

Open-Access Single-Session Therapy in the Context of Stepped Care 2.0

2020· article· en· W3103598494 on OpenAlexaffvenueabout
Peter Cornish, AnnMarie Churchill, Heather Hair

Bibliographic record

VenueJournal of Systemic Therapies · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSession (web analytics)Mental healthContext (archaeology)PsychologyPopulationProcess (computing)Health careWork (physics)Process managementNursingComputer scienceBusinessMedicinePsychiatryEngineeringEconomic growthGeographyEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

Single-session, or one-at-a-time, open-access care is central to the mental health care system transformation process now underway in several parts of Canada. Single-session principles align well with a mental health recovery strategy and the new Stepped Care 2.0 (SC2.0) model under consideration across Canada. SC2.0 provides a framework for integrating single-session open-access care within the broader mental health ecosystem. Through continuous co-design the model connects values and addresses inevitable tensions that arise when attempting system integration. Model development and scaling is still in the early stages. More work needs to be done, including both program evaluation and independent research. Continued critical attention is the only way to maximize impact at a population level. With open access to an array of resources, organized to meet people where they are in terms of readiness, functioning and capacity for engagement, population mental health is possible. This paper highlights the importance and effectiveness of open-access counseling in a stepped care framework.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0030.004
Scholarly communication0.0030.002
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.434
GPT teacher head0.485
Teacher spread0.051 · 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 designNot applicable
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

Citations4
Published2020
Admission routes3
Has abstractyes

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