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Record W2120301286 · doi:10.5539/gjhs.v6n3p27

Development of Evidence-Based Health Policy Documents in Developing Countries: A Case of Iran

2014· article· en· W2120301286 on OpenAlexvenueno aff
Mohammad Hasan Imani-Nasab, Hesam Seyedin, Reza Majdzadeh, Bahareh Yazdizadeh, Masoud ‎Salehi

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersMinistry of Health and Medical EducationIran University of Medical Sciences
KeywordsSnowball samplingNormativeHealth policyQualitative researchNonprobability samplingEmpowermentExploratory researchPublic relationsPsychologyBusinessNursingMedicinePolitical scienceSociologyPublic healthEconomic growthEconomicsEnvironmental healthSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Evidence-based policy documents that are well developed by senior civil servants and are timely available can reduce the barriers to evidence utilization by health policy makers. This study examined the barriers and facilitators in developing evidence-based health policy documents from the perspective of their producers in a developing country. METHODS: In a qualitative study with a framework analysis approach, we conducted semi-structured interviews using purposive and snowball sampling. A qualitative analysis software (MAXQDA-10) was used to apply the codes and manage the data. This study was theory-based and the results were compared to exploratory studies about the factors influencing evidence-based health policy-making. RESULTS: 18 codes and three main themes of behavioral, normative, and control beliefs were identified. Factors that influence the development of evidence-based policy documents were identified by the participants: behavioral beliefs included quality of policy documents, use of resources, knowledge and innovation, being time-consuming and contextualization; normative beliefs included policy authorities, policymakers, policy administrators, and co-workers; and control beliefs included recruitment policy, performance management, empowerment, management stability, physical environment, access to evidence, policy making process, and effect of other factors. CONCLUSION: Most of the cited barriers to the development of evidence-based policy were related to control beliefs, i.e. barriers at the organizational and health system levels. This study identified the factors that influence the development of evidence-based policy documents based on the components of the theory of planned behavior. But in exploratory studies on evidence utilization by health policymakers, the identified factors were only related to control behaviors. This suggests that the theoretical approach may be preferable to the exploratory approach in identifying the barriers and facilitators of a behavior.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0070.006
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.643
GPT teacher head0.682
Teacher spread0.040 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations27
Published2014
Admission routes1
Has abstractyes

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