Using the <scp>H</scp>ealth <scp>B</scp>elief <scp>M</scp>odel to explain patient involvement in patient safety
Bibliographic record
Abstract
BACKGROUND: With the knowledge that patient safety incidents can significantly impact patients, providers and health-care organizations, greater emphasis on patient involvement as a means to mitigate risks warrants further research. OBJECTIVE: To understand whether patient perceptions of patient safety play a role in patient involvement in factual and challenging patient safety practices and whether the constructs of the Health Belief Model (HBM) help to explain such perceptions. DESIGN: Partial least squares (PLS) analysis of survey data. SETTING AND PARTICIPANTS: Four inpatient units located in two tertiary hospitals in Atlantic Canada. Patients discharged from participating units between November 2010 and January 2011. INTERVENTION: None. RESULTS: A total of 217 of the 587 patient surveys were returned for a final response rate of 37.0%. The PLS analysis revealed relationships between patient perceptions of threat and self-efficacy and the performance of factual and challenging patient safety practices, explaining 46 and 42% of the variance, respectively. DISCUSSION: The results from this study provide evidence for the constructs and relationships set forth by the HBM. Perceptions of patient safety were shown to influence patient likelihood for engaging in selected patient safety practices. While perceptions of barriers and benefits and threats were found to be a contributing factor to patient involvement in patient safety practices, self-efficacy plays an important role as a mediating factor. CONCLUSIONS: Overall, the use of the HBM within patient safety provides for increased understanding of how such perceptions can be influenced to improve patient engagement in promoting safer health care.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".