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Decision‐making needs of patients with depression: a descriptive study

2008· article· en· W2087138569 on OpenAlexaffabout
Dawn Stacey, Prudence Menard, Isabelle Gaboury, Mary Jane Jacobsen, Farkhondeh Sharif, Lisa M. Puchalski Ritchie, Helen Bunn

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsRoyal Ottawa Mental Health CentreChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsEmbarrassmentFeelingDepression (economics)DistressMedicineAnxietyConfidentialityPsychiatryFamily medicinePsychologyNursingClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The study's purpose was to explore the decision-making needs of patients considering treatment options for their depression. Semi-structured interviews were guided by the Ottawa Decision Support Framework. Of 94 participants, 67 were uncertain about their decision. Common decisions identified were whether or not to take medications, attend support groups, undergo electroconvulsive therapy, and location of care. Those feeling certain were more likely to have made a decision (RR 1.37; 95% CI: 1.05, 1.78). However, 40 patients who had 'made a decision' in the recent past were uncertain about their decision. Compared with those who were certain, the uncertain group felt less informed (2.65 vs. 1.64; P < 0.001), less supported (2.63 vs. 1.88; P < 0.001) and less clear about how they valued the benefits and risks of options (2.57 vs. 1.69; P < 0.001). Other influential factors included concerns about confidentiality, distress from depression, embarrassment, panic attacks and lack of energy. Few patients wanted to defer decision making to their physician (n = 8) or family (n = 1). To support decision making, participants identified the need for: discussions with their psychiatrist, nurse or family doctor; access to printed information; and information provided by health professionals and health societies.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.422
Teacher spread0.326 · 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.

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

Citations39
Published2008
Admission routes2
Has abstractyes

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