Reframe the pain: Divided attention and positive memory reframing to reduce needle pain and distress in children—A feasibility randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Negative experiences of needle procedures in childhood can lead to medical avoidance and vaccine hesitancy into adulthood. We evaluated the feasibility of two new interventions provided by clinical nurses to reduce the negative impact of vaccinations: divided attention (DA) and positive memory reframing (PMR). METHODS: Children (8-12 years) were randomized into four groups: usual care (UC), DA, PMR or combined (DA + PMR). To evaluate feasibility, we undertook in-depth analysis of video-recorded interventions, nurse experiences (phone interviews) and child/parent memory recall of interventions (phone interviews at 2 weeks post-vaccination). Key clinical outcomes included child and parent ratings of needle-related pain intensity and fear assessed at baseline, immediately post-vaccination and 2 weeks post-vaccination (recalled). RESULTS: A total of 54 child-parent dyads were screened, with 41 included (10/group, except PMR [n = 11]). The interventions were not always completed as intended: 10%-22% of participants received complete interventions and two had adverse events related to protocol breach. Preliminary within-group analyses showed no effects on child/parent pain ratings. However, children in DA + PMR had reduced recalled fear (p = 0.008), and PMR (p = 0.025) and DA + PMR (p = 0.003) had reduced fear of future needles. Parent ratings of child fear were also reduced immediately post-vaccination for UC (p = 0.035) and PMR (p = 0.035). CONCLUSIONS: The interventions were feasible, although enhanced nurse training is required to improve fidelity. Preliminary clinical results appear promising, particularly for reducing needle-related fear. PROTOCOL REGISTRATION: Protocol number ACTRN12618000687291 at ANZCTR.org.au SIGNIFICANCE: Two new nurse-led interventions to reduce negative impacts of vaccinations in children, divided attention and positive memory reframing, were feasible and may reduce needle-related fear. Nurses were able to deliver the interventions in various environments including non-clinical settings (schools). These interventions have potential to facilitate broader dissemination of vaccinations for children in a manner that minimizes distress.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".