Implementation Intentions as a Strategy to Increase the Notification Rate of Potential Ocular Tissue Donors by Nurses: A Clustered Randomized Trial in Hospital Settings
Bibliographic record
Abstract
Aim. The purpose of this study is to evaluate the impact, among nurses in hospital settings, of a questionnaire-based implementation intentions intervention on notification of potential ocular tissue donors to donation stakeholders. Methods. This randomized intervention was clustered at the level of hospital departments with two study arms: questionnaire-based implementation intentions intervention and control. In the intervention group, nurses were asked to plan specific actions if faced with a number of barriers when reporting potential ocular donors. The primary outcome was the potential ocular tissue donors' notification rate before and after the intervention. Analysis was based on a generalized linear model with an identity link and a binomial distribution. Results. We compared outcomes in 26 departments from 5 hospitals, 13 departments per condition. The implementation intentions intervention did not significantly increase the notification rate of ocular tissue donors (intervention: 23.1% versus control: 21.1%; χ (2) = 1.14, 2; P = 0.56). Conclusion. A single and brief implementation intentions intervention among nurses did not modify the notification rate of potential ocular tissue donors to donation stakeholders. Low exposure to the intervention was a major challenge in this study. Further studies should carefully consider a multicomponent intervention to increase exposure to this type of intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".