A multiple-behaviour investigation of goal prioritisation in physicians receiving audit and feedback to address high-risk prescribing in nursing homes
Bibliographic record
Abstract
BACKGROUND: As part of their professional role, healthcare providers enact multiple competing goal-directed behaviours in time-constrained environments. Better understanding healthcare providers' motivation to engage in the pursuit of particular goals may help inform the development of implementation interventions. We investigated healthcare providers' pursuit of multiple goals as part of a trial evaluating the effectiveness of an audit and feedback intervention in supporting appropriate adjustment of high-risk medication prescribing by physicians working in nursing homes. Our objectives were to determine whether goal priority and constructs from Social Cognitive Theory (self-efficacy, outcome expectations, and descriptive norms) predicted intention to adjust prescribing of multiple high-risk medications and to investigate how physicians in nursing homes prioritise their goals related to high-risk medication prescribing. METHODS: Physicians in Ontario, Canada, who signed up for and accessed the audit and feedback report were invited to complete a questionnaire assessing goal priority, self-efficacy, outcome expectations, descriptive norms, and intention in relation to the three targeted behaviours (adjusting prescribing of antipsychotics, benzodiazepines, and antidepressants) and a control behaviour (adjusting statin prescribing). We conducted multiple linear regression analyses to identify predictors of intention. We also conducted semi-structured qualitative interviews to investigate how physicians in nursing homes prioritise their goals in relation to appropriately adjusting prescribing of the medications included in the report: analysis was informed by the framework analysis method. RESULTS: Thirty-three of 89 (37%) physicians completed the questionnaire. Goal priority was the only significant predictor of intention for each medication type; the greater a priority it was for physicians to appropriately adjust their prescribing, the stronger was their intention to do so. Across five interviews, physicians reported prioritising adjustment of antipsychotic prescribing specifically. This was influenced by negative media coverage of antipsychotic prescribing in nursing homes, the provincial government's mandate to address antipsychotic prescribing, and by the deprescribing initiatives or best practice routines in place in their nursing homes. CONCLUSIONS: Goal priority predicted nursing home physicians' intention to adjust prescribing. Targeting goal priority through implementation interventions therefore has the potential to influence behaviour via increased motivation. Implementation intervention developers should consider the external factors that may drive physicians' prioritization.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".