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Record W4380270455 · doi:10.1515/9780773590212-018

Service Use in an Outpatient Clinic for Current and Veteran Military and RCMP Members

2013· book-chapter· en· W4380270455 on OpenAlexaboutno aff
Jennifer C. Laforce, Debbie Whitney, Kristen Klassen

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2013
Typebook-chapter
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineService (business)Military serviceMedical diagnosisMilitary personnelMental healthType of serviceService memberFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

Military, Veteran, and RCMP clients engage in a variety of outpatient mental health treatments through the Winnipeg OSI Clinic. Similar to most outpatient settings, service use is mutually regulated by clinicians and clients. Files of 393 discharged clients were reviewed in order to determine typical service use and the factors that predict the number of sessions received. Clinical symptom severity did not differentiate the type of services in which clients engaged. Retired, veteran, older clients were overrepresented in the group that received a formal assessment but no therapy, whereas current Canadian Forces members were much more likely to engage in therapy than use assessment services only. In predicting volume of service use, it appears important to attend to diagnostic complexity as knowing clients’ diagnoses, and in particular co-morbidities, provided significantly more information than just being aware of the intensity of their reported symptoms or demographic variables.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.932
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.002

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 head0.069
GPT teacher head0.314
Teacher spread0.245 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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