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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".