Nursing-sensitive outcomes for the provision of pain management in pediatric populations with intellectual disabilities: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The aim of this review is to identify and map nursing-sensitive outcomes for the provision of pain management in pediatric populations with intellectual disabilities that are currently reported in the literature. INTRODUCTION: The experience of pain is highly individualized and subjective, with physiological, biochemical, and psychological differences contributing to pain perception. Pediatric populations with intellectual disabilities are at increased risk of ubiquitous pain exposure. Pain management effectiveness can be determined through the measurement of nursing-sensitive outcomes, which have not been mapped in the context of pediatric populations with intellectual disabilities. INCLUSION CRITERIA: Quantitative, qualitative, mixed methods, and gray literature discussing nursing pain management in pediatric populations with intellectual disabilities will be included. No date limits will be applied. Only studies published in English will be considered. METHODS: This review will be guided by the JBI methodology for scoping reviews. The search strategy will aim to locate published and unpublished literature using the databases CINAHL (EBSCOhost), MEDLINE (Ovid), Embase (Ovid), Scopus, PsycINFO (ProQuest), LILACS, SciELO, and ProQuest Dissertations and Theses Global. Titles and abstracts, and then full-text studies, will be selected and reviewed by 2 independent researchers against the inclusion criteria. Content analysis using the NNQR-C, C-HOBIC, NDNQI, and Donabedian model frameworks will be used for data extraction and organization, accompanied by charted results and narrative summaries, as appropriate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".