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Record W2148964916 · doi:10.3200/bmed.32.4.127-134

Predictors of Self-Reported Antidepressant Adherence

2007· article· en· W2148964916 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehavioral Medicine · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity Health NetworkYork UniversityUniversity of Toronto
Fundersnot available
KeywordsAntidepressantPsychiatryMedicinePsychologyClinical psychologyDepression (economics)Anxiety

Abstract

fetched live from OpenAlex

The authors' objectives of this research were: (1) to assess levels of selfreported antidepressant adherence and reasons for nonadherence and (2) to investigate determinants of nonadherence. A group of general hospital and community psychiatry practice mood disorder outpatients (n=80) took a self-report questionnaire that assessed beliefs about antidepressants, self-efficacy, and reasons for nonadherence. High levels of adherence were reported: 58 patients (73%) indicated they took their medication as directed more than 80% of the time. Practical issues (e.g., simply forgetting or a change in routine) were the most frequently identified reasons for nonadherence. Patients were more likely to report nonadherence if they experienced a sexual side effect, had lower self-efficacy, were female, and had not completed post-secondary education. Clinicians should be cognizant of this complexity and address not only issues related to medication efficacy and tolerability, but also social mediators and health beliefs when prescribing antidepressants.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.999

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)0.0020.000

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 head0.076
GPT teacher head0.429
Teacher spread0.353 · 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