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Record W2017469390 · doi:10.5539/gjhs.v7n3p260

Family Medicine in Iran: Facing the Health System Challenges

2014· article· en· W2017469390 on OpenAlexvenueno aff
Reza Esmaeili, Mohammad Reza Hadian, Arash Rashidian, Mohammad Shariati, Hossien Ghaderi

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersIran University of Medical Sciences
KeywordsMedicineFamily medicineTraditional medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In response to the current fragmented context of health systems, it is essential to support the revitalization of primary health care in order to provide a stronger sense of direction and integrity. Around the world, family medicine recognized as a core discipline for strengthening primary health care setting. OBJECTIVE: This study aimed to understand the perspectives of policy makers and decision makers of Iran's health system about the implementation of family medicine in Iran urban areas. MATERIALS/PATIENTS & METHODS: This study is a qualitative study with framework analysis. Purposive semi-structured interviews were conducted with Policy and decision makers in the five main organizations of Iran health care system. The codes were extracted using inductive and deductive methods. RESULTS: According to 27 semi-structured interviews were conducted with Policy and decision makers, three main themes and 8 subthemes extracted, including: The development of referral system, better access to health care and the management of chronic diseases. CONCLUSION: Family medicine is a viable means for a series of crucial reforms in the face of the current challenges of health system. Implementation of family medicine can strengthen the PHC model in Iran urban areas. Attempting to create a general consensus among various stakeholders is essential for effective implementation of the project.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.472
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2014
Admission routes1
Has abstractyes

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