Teaching Parents to Manage Pain During Infant Immunizations
Bibliographic record
Abstract
OBJECTIVE: To evaluate knowledge uptake from a parent-directed factsheet about managing pain during infant vaccinations, and the added influence of a pretest. MATERIALS AND METHODS: Solomon 4-group randomized controlled trial. New mothers hospitalized after the birth of an infant were randomized to 1 of 4 groups: 2 included the intervention (factsheet about pain management) and 2 included the control (information on another topic). A pretest was given to 1 intervention and 1 control group. Following maternal review of allocated information, posttests were administered in all groups. Both control groups received the information after posttesting. A follow-up telephone survey after 2 months measured knowledge retention and utilization of pain management interventions. RESULTS: A total of 120 mothers participated (July, 2012 to February, 2013); demographics did not differ among groups. The 2 factsheet groups demonstrated more knowledge (P<0.05) about effective pain management (mean without pretest: 5.6 [SD=2.0]; with pretest: 6.9 [1.6]) compared with the 2 control groups (without pretest: 3.2 [2.2]; with pretest: 3.4 [2.5]) immediately after review; and the factsheet and pretest group scored higher than the factsheet only group. In groups with a prefactsheet baseline knowledge test, knowledge was higher at follow-up compared with baseline. Follow-up knowledge and utilization of pain management interventions did not differ among groups. CONCLUSIONS: The factsheet led to acute gains in knowledge and knowledge gains persisted after 2 months. Acutely, knowledge was bolstered by the pretest. These results can be used to guide future research and implementation of the factsheet.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".