MétaCan
Menu
Back to cohort
Record W1967917164 · doi:10.1186/1748-5908-4-73

Adjuncts or adversaries to shared decision-making? Applying the Integrative Model of behavior to the role and design of decision support interventions in healthcare interactions

2009· article· en· W1967917164 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

VenueImplementation Science · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychological interventionDeliberationFormative assessmentDecision aidsNormativeHealth careHealth informaticsApplied psychologyHealth administrationPsychologyHealth services researchTheory of planned behaviorMedicineKnowledge managementPublic healthNursingComputer scienceControl (management)Alternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A growing body of literature documents the efficacy of decision support interventions (DESI) in helping patients make informed clinical decisions. DESIs are frequently described as an adjunct to shared decision-making between a patient and healthcare provider, however little is known about the effects of DESIs on patients' interactional behaviors-whether or not they promote the involvement of patients in decisions. DISCUSSION: Shared decision-making requires not only a cognitive understanding of the medical problem and deliberation about the potential options to address it, but also a number of communicative behaviors that the patient and physician need to engage in to reach the goal of making a shared decision. Theoretical models of behavior can guide both the identification of constructs that will predict the performance or non-performance of specific behaviors relevant to shared decision-making, as well as inform the development of interventions to promote these specific behaviors. We describe how Fishbein's Integrative Model (IM) of behavior can be applied to the development and evaluation of DESIs. There are several ways in which the IM could be used in research on the behavioral effects of DESIs. An investigator could measure the effects of an intervention on the central constructs of the IM - attitudes, normative pressure, self-efficacy, and intentions related to communication behaviors relevant to shared decision-making. However, if one were interested in the determinants of these domains, formative qualitative research would be necessary to elicit the salient beliefs underlying each of the central constructs. Formative research can help identify potential targets for a theory-based intervention to maximize the likelihood that it will influence the behavior of interest or to develop a more fine-grained understanding of intervention effects. SUMMARY: Behavioral theory can guide the development and evaluation of DESIs to increase the likelihood that these will prepare patients to play a more active role in the decision-making process. Self-reported behavioral measures can reduce the measurement burden for investigators and create a standardized method for examining and reporting the determinants of communication behaviors necessary for shared decision-making.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.397
GPT teacher head0.583
Teacher spread0.186 · 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