Maternal kisses are not effective in alleviating minor childhood injuries (boo‐boos): a randomized, controlled and blinded study
Bibliographic record
Abstract
BACKGROUND: The practice of maternal kissing of minor injuries of childhood (boo-boos), though widely endorsed and practised, has never been demonstrated to be of benefit to children. OBJECTIVE: To determine the efficacy, if any, of maternal kissing of boo-boos in toddlers. DESIGN: Randomized, controlled and double-blinded study of children with experimentally induced minor injuries. Control arms included both no intervention group and 'sham' (non-maternal) kissing. Children were blinded to the identity of the kisser in both the maternal and sham control groups. SETTING: Outpatient research clinics in Ottawa, Canada. PARTICIPANTS: 943 maternal-toddler pairs recruited from the community. MEASUREMENTS: Toddler Discomfort Index (TDI) pre-injury, 1 and 5 minutes post-injury. RESULTS: One-minute and 5-minute TDI scores did not differ significantly between the maternal and sham kiss groups. Both of these groups had significantly higher TDI scores at 5 minutes compared to the no intervention group. CONCLUSIONS: Maternal kissing of boo-boos confers no benefit on children with minor traumatic injuries compared to both no intervention and sham kissing. In fact, children in the maternal kissing group were significantly more distressed at 5 minutes than were children in the no intervention group. The practice of maternal kissing of boo-boos is not supported by the evidence and we recommend a moratorium on the practice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".