Fragmentation of healthcare systems: challenges through patients’ eyes
Bibliographic record
Abstract
INTRODUCTION: This article aims to identify key challenges raised by fragmented healthcare systems by adopting the lens of patients. We analyzed how high fragmentation leads to the loss of humanity from healthcare providers as perceived by the parents of children born with disabilities. METHODS: Twenty-nine Lebanese families of children born with disability agreed to participate. Data was collected through semi-structured interviews, recorded and then coded using Nvivo 11. The coding followed an abductive approach. RESULTS AND CONTRIBUTION: The study revealed the financial and bureaucratic burdens, the reification and relational challenges of fragmented healthcare systems. We identified how parents became an expert patient and a care path manager. Our contribution is by addressing the fragmentation issue from the parents' point of view. In this way, we further contribute to documenting the impact of fragmentation, on the lives of patients and their families and the intensity of the violence generated by the institutional system that amplifies the trauma inherent to the clinical case which refers to the chronic emotional and psychological distress that parents endure as they repeatedly confront care discontinuities, administrative barriers, and the need to compensate for the system's failures.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".