Dental care provision for people with neurodevelopmental disorders in Iran; a qualitative study of barriers
Bibliographic record
Abstract
INTRODUCTION: One of the most underserved populations among dental patients is the people with Neurodevelopmental Disorders (PNDs). This study aimed to explore the barriers to dental care provision for PNDs from the viewpoint of stakeholders in provision in Iran. METHODS: We arranged a qualitative study based on an individual in-depth, semi-structured interview between October 2019 and February 2020. We interviewed 30 participants using a snowball sampling strategy with three groups: dentists who provided dental care for PNDs, dental public health professionals, and policymakers/managers in dental care provision in Iran. The interviewer used an interview framework based on Levesque's model of patient-centered access. The main question was: "How do you assess the barriers to the provision of dental services to PNDs in Iran?" The data were analyzed by the qualitative content analysis method described by Graneheim and Lundman. RESULTS: From the 30 interviewees (11 women), there were 11 dentists, nine dental public health professionals, and 10 participants who worked as policymakers/managers. They were aged 35-62 years and had working experience between 4 and 25 years. In the content analysis, 60 meanings units were extracted, and later classified into 14 subthemes, and four main themes. CONCLUSION: Four main barriers were traced regarding access, financial, competency-related, and policy-making aspects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".