Healthcare professionals’ understanding of children’s rights: a systematic review of the empirical evidence-base
Bibliographic record
Abstract
BACKGROUND: The concept of children's rights emerged during the 1980s and emphasised the role of children as active participants in matters which concern them. AIM: This review aims to identify and synthesise the empirical evidence base on healthcare professionals' (HCPs) understanding of children's rights. METHODS: Five electronic databases (PubMed, CINAHL, Embase, PsycINFO, and the Web of Science) were systematically searched in May 2023. The Mixed Methods Appraisal Tool (MMAT) was used to quality appraise full-text papers included in the review. A descriptive narrative synthesis of the studies' findings was performed. RESULTS: A total of 15 relevant studies from 10 countries were identified and included in the review. The number of participants included ranged from 6 to 1048 for HCPs with a broad range of sampling methods. Based on the narrative synthesis of the included studies, three main themes were identified: (1) Barriers to implementing children's rights in healthcare, (2) Factors that contribute to children's rights implementation, and (3) Study instruments used to measure outcomes. CONCLUSIONS: HCPs require a better understanding of children's rights to implement these rights into practice. Listening to children, building trusting relationships with children, and continuing professional development of HCPs could help to address barriers to understanding children's rights. There is a pressing need for the development of a tool that is capable of tracking changes in the understanding of children's rights in healthcare environments as efforts to increase awareness become more widely recognised.
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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 | 0.031 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".