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Record W3030899177 · doi:10.1093/sleep/zsaa056.509

0512 Impact of a Patient Decision-Aid When Selecting Insomnia Treatments and Factors Associated with Decisional Conflict: Preliminary Findings from an Ongoing Pragmatic Clinical Trial

2020· article· en· W3030899177 on OpenAlexaffabout
Janet M. Y. Cheung, Xiaowen Ji, Hans Ivers, Charles M. Morin

Bibliographic record

VenueSLEEP · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsInsomniaPopulationMedicineIntervention (counseling)Randomized controlled trialClinical trialClinical psychologyPsychologyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Preferences play an important role in determining insomnia treatment outcomes, but the validity of patient choice is rarely assessed. Uninformed preferences can lead to decisional conflict, which can negatively impact on treatment initiation, adherence, and subsequent outcomes. The current study aims to evaluate the impact of integrating a patient decision-aid as part of a pragmatic clinical trial and to identify baseline covariates associated with clinically significant decisional conflict (CSDC). Methods Secondary analysis of an ongoing pragmatic clinical trial for a two-stage cognitive behavioral therapy for insomnia (CBT-I) intervention was undertaken. Participants were referred from primary care clinics in Quebec City, Canada. Upon enrolment, participants were guided by a decision-aid, outlining the risks and benefits of prospective treatment options, when selecting their preferred arm of treatment in Phase 1. Options included SHUTi, SHUTi combined with an existing medication or continuing usual treatment with medication alone. Participants also completed a battery of sleep and mental health measures at baseline. Prior to treatment initiation, the 4-item SURE (Sure of myself; Understand information; Risk-Benefit ratio; Encouragement) scale was administered to screen for CSDC. Relationships between CSDC and baseline covariates were explored using Pearson correlations. Results Of the 55 participants initially enrolled, 94.5% (n=52) of participants preferentially selected SHUTi, either as sole treatment (n=24) or in combination with an existing medication (n=28), over usual treatment with medication alone (n=3). Overall, CSDC was only reported by 5.5% (n=3) of the sample population, with no group differences observed, suggesting effective clarification of treatment options through the decision-aid. Interestingly, higher SURE scores (i.e. less decisional conflict) were negatively correlated with depressive symptoms (r= -0.295, n= 55, p= 0.029) and anxiety symptoms (r= -0.301, n= 55, p= 0.026). Correlations with age, insomnia symptoms, duration of insomnia and fatigue were not statistically significant. Conclusion The patient decision-aid appeared to resolve decisional conflict for 94.5% (n=52) of participants. Findings allude to the potential influence of emotional status on information processing pathways in an insomnia context, warranting further research. Support Research supported by a grant from the Canadian Institutes of Health Research (CIHR-IRSC:0441002152).

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.135
GPT teacher head0.434
Teacher spread0.299 · 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 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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