Usability and knowledge testing of educational tools about infant vaccination pain management directed to postnatal nurses
Bibliographic record
Abstract
BACKGROUND: Adapting educational tools to meet user needs is a critical aspect of translating research evidence into best clinical practices. The objectives of this study were to evaluate usability and effectiveness of educational tools about infant vaccination pain management directed to postnatal nurses. METHODS: Mixed methods design. A template pamphlet and video included in a published clinical practice guideline were subjected to heuristic usability evaluation and then the revised tools were reviewed by postnatal hospital nurses in three rounds of interviews involving 8 to 12 nurses per round. Nurses' knowledge about evidence-based pain management interventions was evaluated at three time points: baseline, after pamphlet review, and after video review. RESULTS: Of 32 eligible postnatal nurses, 29 agreed to participation and data were available for 28. Three overarching themes were identified in the interviews: 1) utility of information, 2) access to information, and 3) process for infant procedures. Nurses' knowledge improved significantly (p < 0.05) from the baseline phase to the pamphlet review phase, and again from the pamphlet review phase to the video review phase. CONCLUSIONS: This study demonstrated usability and knowledge uptake from a nurse-directed educational pamphlet and video about managing infant vaccination pain. Future studies are needed to determine the impact of implementing these educational tools in the postnatal hospital setting on parental utilization of analgesic interventions during infant hospitalization and future infant vaccinations.
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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 | 0.042 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".