Understanding medical neglect: Perspectives of Palestinian youth from East Jerusalem
Bibliographic record
Abstract
Background Child medical neglect is defined as the failure to provide timely healthcare to the child, placing them at risk of harm. Despite Israel's universal health coverage, East Jerusalem (EJ) Palestinian youth and their families face unique socioeconomic realities and inequitable healthcare access that are shaped by the broader socio-political climate and policies that further challenge healthcare provision. Engaging EJ youth in research, as potentially vulnerable to medical neglect and as experts in their lives, fills a current gap in understanding their specific experiences and healthcare needs. Objective This qualitative inquiry explored EJ Palestinian youths' perceptions of medical neglect via review and discussion of medical neglect case vignettes. Participants and setting Arab-speaking Palestinian youth aged 12 to 14 were recruited from two EJ schools to participate in data collection activities. Data collection was facilitated by the research team in a confidential space within local EJ school libraries. Methods We addressed our qualitative research aims via semi-structured focus groups, one each for boys and girls who participated in the study. Both parental consent and youth assent were obtained before youth participation. Open-ended questions for each focus group featured questions about healthcare and medical neglect, youth reflections on three medical neglect vignettes, and healthcare advocacy questions. Discussions were transcribed, translated into English, and subjected to inductive thematic analysis. Results A total of 14 youth participated in the study's focus groups, one each for boys and girls (57%). The youth demonstrated an awareness of their lived environments, socio-political contexts, cultural influences, and rights-based expectations regarding caregiving. The five main themes included perceived severity and chronicity of medical neglect, emotional impact, systemic factors, shared responsibility, and valuing the child's perspective. Conclusion EJ youth understand the complex challenges and competing needs within their community. Findings point to the potential role of youth engagement in research related to healthcare systems, particularly in violence-risk living environments. This study encourages further research to amplify youth voices within complex sociopolitical contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".