Understanding the nursing practices and perspectives of transfusion reaction reporting
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The aim of this study was to investigate nurse perspectives on transfusion-related adverse reaction reporting practices. BACKGROUND: Transfusion-related adverse reaction reporting is an essential component of hemovigilance in Canada, but reporting rates vary and under-reporting of minor transfusion-related adverse reactions exists. To our knowledge, this is the first report of nursing transfusion-related adverse reaction reporting attitudes. DESIGN: This qualitative descriptive study explored the nursing practices and perspectives of transfusion-related adverse reaction reporting by conducting one-on-one interviews with nurses (n = 25) working in adult oncology inpatient and outpatient units. METHODS: Data were thematically analysed; data collection ended when saturation was reached. The COREQ checklist was used to guide this study. RESULTS: The study revealed that the nursing practices of transfusion-related adverse reaction reporting are not standardised to meet the institutional reporting guidelines. Under-reporting of febrile reactions exists at this institution. Major concepts uncovered included the factors impacting nurses' transfusion-related reporting practices, as well as barriers and facilitators to transfusion reporting. CONCLUSION: A practice change in transfusion-related adverse reaction reporting is needed to achieve optimal hemovigilance at this institution. Using the barriers and facilitators identified in this study, institutions can better inform future interventions by employing strategies like TR reporting education in order to improve reporting of transfusion-related adverse reactions in this hospital and other similar institutions. RELEVANCE TO CLINICAL PRACTICE: This study informs clinical practice and decision-making for nurses and nursing educators who manage blood transfusion administration procedures.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".