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Record W3162899421 · doi:10.1097/jom.0000000000002248

An Open Trial of the Effectiveness, Program Usage, and User Experience of Internet-based Cognitive Behavioural Therapy for Mixed Anxiety and Depression for Healthcare Workers on Disability Leave

2021· article· en· W3162899421 on OpenAlexaff
Andrew Miki, Mark A. Lau, Hoora Moradian

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnxietyDepression (economics)Health careMedicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: An open trial of an internet-based Cognitive Behavioural Therapy (iCBT) program for healthcare workers was conducted. METHODS: Healthcare workers on disability leave who used the iCBT program were assessed on: self-reported depression and anxiety symptoms using the Depression Anxiety Stress Scales-21; and, program usage. Healthcare workers' experience of using iCBT was evaluated in a separate survey. RESULTS: Of the 497 healthcare workers referred to the iCBT program, 51% logged in, 25% logged in more than once, and 12% logged in more than once and completed at least two assessments. For the latter group, self-reported depression and anxiety symptoms significantly decreased from the first assessment. CONCLUSIONS: This iCBT program was perceived to be of benefit to healthcare workers, with program usage and effectiveness that was similar to what has been previously reported for unguided iCBT.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.444
Teacher spread0.375 · 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 designNon-randomized trial
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

Citations9
Published2021
Admission routes1
Has abstractyes

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