Too many crying babies: a systematic review of pain management practices during immunizations on YouTube
Bibliographic record
Abstract
BACKGROUND: Early childhood immunizations, although vital for preventative health, are painful and too often lead to fear of needles. Effective pain management strategies during infant immunizations include breastfeeding, sweet solutions, and upright front-to-front holding. However, it is unknown how often these strategies are used in clinical practice. We aimed to review the content of YouTube videos showing infants being immunized to ascertain parents' and health care professionals' use of pain management strategies, as well as to assess infants' pain and distress. METHODS: A systematic review of YouTube videos showing intramuscular injections in infants less than 12 months was completed using the search terms "baby injection" and "baby vaccine" to assess (1) the use of pain management strategies and (2) infant pain and distress. Pain was assessed by crying duration and pain scores using the FLACC (Face, Legs, Activity, Cry, Consolability) tool. RESULTS: A total of 142 videos were included and coded by two trained individual viewers. Most infants received one injection (range of one to six). Almost all (94%) infants cried before or during the injections for a median of 33 seconds (IQR = 39), up to 146 seconds. FLACC scores during the immunizations were high, with a median of 10 (IQR = 3). No videos showed breastfeeding or the use of sucrose/sweet solutions during the injection(s), and only four (3%) videos showed the infants being held in a front-to-front position during the injections. Distraction using talking or singing was the most commonly used (66%) pain management strategy. CONCLUSIONS: YouTube videos of infants being immunized showed that infants were highly distressed during the procedures. There was no use of breastfeeding or sweet solutions and limited use of upright or front-to-front holding during the injections. This systematic review will be used as a baseline to evaluate the impact of future knowledge translation interventions using YouTube to improve pain management practices for infant immunizations.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".