Publicly Available Online Educational Videos Regarding Pediatric Needle Pain
Bibliographic record
Abstract
OBJECTIVES: The objectives of this scoping review were to: (1) identify publicly available educational videos on needle pain management; and (2) evaluate the content of these videos. METHODS: Reviewers screened publicly available educational videos on pediatric needle pain management available on YouTube and Google using a broad-based search strategy. Videos were categorized using the CRAAP Test: Current, Relevant, from a trustworthy source (Authority), Accurate and evidence-based, and for what Purpose does the source exist. RESULTS: Twenty-five relevant, educational videos were identified. The intended audience for most videos was parents (n=16, 64%), followed by clinicians (n=6, 24%) and children (n=3, 12%). Common examples of needle pain included immunizations or IV insertion, with interventions appropriate for infants through school-aged children. The most frequently described techniques were parent-guided distraction and behavioral factors such as comfort holds and parent demeanor. Most videos were Current (96%), Relevant (100%), created by a trustworthy source: Authority (76%), and all were Accurate, with Purpose relating to needle pain management. None of the videos addressed the unique needs of children with a preexisting diagnosis of needle phobia. DISCUSSION: Publicly available educational videos offer clinicians, parents, and children evidence-based techniques to manage pediatric needle pain. Further evaluation is needed to determine whether this form of education meets the needs of target audiences and whether this type of content can lead to improved management of pediatric needle pain.
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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 | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".