Adoption and sustainability of decision support for patients facing health decisions: an implementation case study in nursing
Bibliographic record
Abstract
BACKGROUND: Effective interventions prepare patients for making values-sensitive health decisions by helping them become informed and clarifying their values for each of the options. However, patient decision support interventions have not been widely implemented and little is known about effective models for delivering them to patients. The purpose of this study was to describe call centre nurses' adoption of a decision support protocol into practice following exposure to an implementation intervention and to identify factors influencing sustainable nursing practice changes. METHODS: Exploratory case study at a Canadian province-wide call centre guided by the Ottawa Model of Research Use. Data sources included a survey of nurses who participated in an implementation intervention (n = 31), 2 focus groups with nurses, interviews with 4 administrators, and a document review. RESULTS: Twenty-five of 31 nurses responded to the survey measuring adoption of decision support in practice. Of the 25 nurses, 11 had used the decision support protocol in their practice within one month of the intervention. Twenty-two of the 25 intended to use it within the next three months. Although some nurses found it challenging to begin using the protocol, most nurses reported that it: a) helped them recognize callers needing decision support; b) changed their approach to handling these calls; and c) was a positive enhancement to their practice. Strategies identified to promote sustainability of practice changes included integration of the decision support protocol in the call centre database, streamlining the patient decision aids for easier use via telephone, clarifying the administrative direction for the call centre's program, creating a call length guideline specific for these calls, incorporating decision support training in the staff development plan, and informing the public of this enhanced service. CONCLUSION: Although most nurses adopted the decision support protocol for coaching callers facing values-sensitive decisions, to sustain practice changes, interventions are required to manage barriers in the practice environment and integrate decision support into the organization's policies, resources, and routine activities.
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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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".