Bridging the gap: Identifying diverse stakeholder needs and barriers to accessing evidence and resources for children’s pain
Bibliographic record
Abstract
BackgroundStakeholder engagement in knowledge mobilization (KMb) activities can bridge the knowledge to action gap within children’s pain but may be influenced by how well stakeholder needs and barriers to evidence-based resources are addressed. The needs of different Canadian stakeholder groups related to children’s pain have not been examined, limiting the degree to which KMb efforts can be tailored to each group.AimsThe study aim was to identify shared and unique needs, barriers, and accessibility of evidence for children’s pain across three stakeholder groups: knowledge users (i.e., health professionals, administrators, policymakers, educators), researchers (including trainees), and patients, caregivers, and family members.MethodsThis study comprised an online needs assessment survey. Analyses included descriptive statistics, one-way analyses of variances, and chi-square tests to examine differences between stakeholder groups. Open-ended responses were analyzed using conventional content analysis.ResultsA total of 711 stakeholders completed the survey. Educational materials were the most utilized evidence-based resources among all stakeholders. Researchers and patients/caregivers/family members found resources significantly less accessible than knowledge users (P = 0.008). Knowledge of evidence was the primary barrier across all stakeholder groups (69.2%, n = 492); however, each group reported a need for stakeholder-specific resources. Finally, stakeholders desired opportunities to engage in the KMb process through partnerships and an increased awareness of children’s pain.ConclusionsThough stakeholders experience common barriers to evidence-based resources for children’s pain, their needs to address these barriers are diverse. Evidence-based resources should be tailored for stakeholders’ contexts, with diverse audiences in mind.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".