Shared Decision Making Interventions: Theoretical and Empirical Evidence with Implications for Health Literacy
Bibliographic record
Abstract
Basic health literacy is required for making health decisions. The aim of this chapter is to discuss the use of shared decision making interventions for supporting patient involvement in making health decisions. The chapter provides a definition of shared decision making and discusses the link between shared decision making and the three levels of health literacy: functional, communicative/interactive, and critical. The Interprofessional Shared Decision Making Model is used to identify the various players involved: the patient, the family/surrogate/significant others, decision coach, and health care professionals. When patients are involved in shared decision making, they have better health outcomes, better healthcare experiences, and likely lower costs. Yet, their degree of involvement is influenced by their level of health literacy. Interventions to facilitate shared decision making are patient decision aids, decision coaching, and question prompt lists. Patient decision aids have been shown to improve knowledge, accurate risk perceptions, and chosen options congruent with patients' values. Decision coaching improves knowledge and patient satisfaction. Question prompts also improve satisfaction. When shared decision making interventions have been evaluated with patients presumed to have lower health literacy, they appeared to be more beneficial to disadvantaged groups compared to those with higher literacy or better socioeconomic status. However, special attention needs to be applied when designing these interventions for populations with lower literacy. Two case exemplars are provided to illustrate the design and choice of interventions to better support patients with varying levels of health literacy. Despite evidence indicating these interventions are effective for involving patients in shared decision making, few are used in routine clinical practice. To increase their uptake, implementation strategies need to overcome barriers interfering with their use. Implementation strategies include training health care professionals, adopting SDM interventions that target patients, such as patient decision aids, and monitor patients' decisional comfort using the SURE test. Integrating health literacy principles is important when developing interventions that facilitate shared decision making and essential to avoid inadvertently producing higher inequalities between patients with varying levels of health literacy.
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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 | 0.024 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".