Telepediatric assistance in Iran: Specialist and subspecialty challenges
Bibliographic record
Abstract
INTRODUCTION: Having justice for easy access to health services may probably be a standard feature and indisputable right of all health policies. The health policy of Iran enunciates this right. Unfortunately, as may happen in many countries, the execution of this policy depends on different factors. Among these parameters should be quoted as the suitable distribution of professionals, hospitals, and medical facilities. On the other hand, in Iran, there are many other problems linked to accessing areas with natural hindrances.OBJECTIVE: In this study, we analyzed the primary studies that report specialty and subspecialty challenges that had been faced by Iran to improve pediatric assistance.METHODS: Conduction of literature search in PubMed and CINAHL libraries was done, especially studies from 2010 to 2019. A Boolean operated ‘MeSH’ term was used for search. Newcastle–Ottawa Scale (NOS) scoring was adopted to assess the quality of each study.RESULTS: A total of 118 studies were displayed, and among them, 102 were excluded due to duplication and study relevance. Study selection was madebased on its content were classified into two groups (1) shortage and unsuitable distribution of specialist and sub-specialist physicians in Iran, and (2) studies that explain the status of degradation in different areas of Iran. Outcomes demonstrated that Iran is generally suffering the shortage and unsuitable distribution of specialists and sub-specialists. This lack is particularly crucial in deprived and far-away areas from the cities.CONCLUSION: The present study analyzed in detail the current data regarding pediatric health services (specialist and subspecialty) and health-specific facilities distribution in the country.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".