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Record W4362665045 · doi:10.1186/s12911-023-02146-y

Development and user testing of a patient decision aid for cancer patients considering treatment for anxiety or depression

2023· article· en· W4362665045 on OpenAlexaboutno aff
Rebecca Rayner, Joanne Shaw, Caroline Hunt

Bibliographic record

VenueBMC Medical Informatics and Decision Making · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCLARITYReferralMedicineAnxietyUsabilityHealth informaticsMental healthPsycho-oncologyHealth professionalsDepression (economics)Health careCancerNursingPsychiatryPublic healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite high rates of mental health disorders among cancer patients, uptake of referral to psycho-oncology services remains low. This study aims to develop and seek clinician and patient feedback on a patient decision aid (PDA) for cancer patients making decisions about treatment for anxiety and/or depression. METHODS: Development was informed by the International Patient Decision Aid Standards and the Ottawa Decision Support Framework. Psycho-oncology professionals provided feedback on the clinical accuracy, acceptability, and usability of a prototype PDA. Cognitive interviews with 21 cancer patients/survivors assessed comprehensibility, acceptability, and usefulness. Interviews were thematically analysed using Framework Analysis. RESULTS: Clinicians and patients strongly endorsed the PDA. Clinicians suggested minor amendments to improve clarity and increase engagement. Patient feedback focused on clarifying the purpose of the PDA and improving the clarity of the values clarification exercises (VCEs). CONCLUSIONS: The PDA, the first of its kind for psycho-oncology, was acceptable to clinicians and patients. Valuable feedback was obtained for the revision of the PDA and VCEs.

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)0.001
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.816
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.322
GPT teacher head0.475
Teacher spread0.153 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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