Facilitators and Barriers to the Implementation of a Digital Pain Assessment Tool in Pediatric Oncology Practice: A Qualitative Evaluation of a Quality Improvement Project
Bibliographic record
Abstract
Background: Most children and adolescents with cancer experience acute pain, and many experience longer-lasting chronic pain, negatively impacting health-related quality of life and resulting in long-term morbidity. Digital apps can aid in enhancing pain assessment and management by offering children and adolescents with cancer an accessible tool to describe their pain as a multifaceted biopsychosocial construct. Pain Squad is a useable, acceptable, and psychometrically sound multidimensional cancer pain assessment app for children and adolescents with cancer. This project aimed to evaluate the capacity to implement Pain Squad into routine pediatric oncology practice. Method: Nurse champions were asked to prescribe the Pain Squad app to patients over a 6-month implementation period. After the implementation period, we conducted audiorecorded, semistructured interviews with nurse champions to investigate the facilitators and barriers related to nurses’ experiences with implementing Pain Squad. Results: The facilitators and barriers to Pain Squad implementation were organized into four overarching Consolidated Framework for Implementation Research (CFIR)-related themes: (a) characteristics of the Pain Squad app; (b) clinic setting and its context; (c) nurse implementation champions; and (d) the process of implementing Pain Squad into clinical practice. Conclusions: Interviewed nurses believed Pain Squad had the potential to improve child cancer pain care, but barriers to everyday use were evident, described in relation to the internal setting, especially the lack of compatibility between app prescription and current nurse workflows. The use of CFIR to map identified implementation facilitators and barriers can formally support the recognition of factors that may boost the chances of successful uptake.
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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 | 0.076 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".