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Record W2143869504 · doi:10.1177/2158244015572486

Adherence to a Telephone-Supported Depression Self-Care Intervention for Adults With Chronic Physical Illnesses

2015· article· en· W2143869504 on OpenAlexaff
Russell Simco, Jane McCusker, Maida Sewitch, Martín G. Cole, Mark J. Yaffe⃰, Kim Lavoie, Tamara Sussman, Erin Strumpf, Antonio Ciampi, Éric Belzile

Bibliographic record

VenueSAGE Open · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversité du Québec à MontréalSt Mary's Hospital CentreMcGill University
Fundersnot available
KeywordsDepression (economics)Intervention (counseling)ComorbidityMedicinePhysical therapyPsychiatryClinical psychologyPsychology

Abstract

fetched live from OpenAlex

We assessed adherence to and predictors of two components of a telephone-supported self-care intervention for depression among primary care adults aged 40 and above with chronic physical illnesses and comorbid depressive symptoms. Participants received a “toolkit” containing six self-care tools. Trained lay self-care “coaches” negotiated a contact schedule of up to weekly contacts. Study outcomes were levels of completion of the self-care tool and the coach contacts at the 2-month follow-up. Coaches reported the number of completed contacts. In all, 57 of 63 participants completed the 2-month follow-up. Of these, 67% completed at least 1 tool; the mean number of coach contacts was 5.7 ( SD = 2.4) of a possible 9 contacts (63% adherence). Higher disease comorbidity and lower initial depression severity independently predicted better tool adherence. Findings suggest that people with chronic physical illnesses can achieve acceptable levels of adherence to a depression self-care intervention similar to those reported for other populations.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.359
Teacher spread0.323 · 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 designOther design
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

Citations3
Published2015
Admission routes1
Has abstractyes

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