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Record W2395780527 · doi:10.5539/gjhs.v9n1p163

Requirements and Incentives for Implementation of Pharmaceutical Strategic Purchasing in Iranian Health System: A Qualitative Study

2016· article· en· W2395780527 on OpenAlexvenueno aff
Peivand Bastani, Leila Doshmangir, Mahnaz Samadbeik, Rassoul Dinarvand

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSnowball samplingPurchasingIncentiveReimbursementBusinessPaymentQualitative researchMarketingPharmaceutical industryProfit marginService providerHealth careService (business)EconomicsMedicineFinance

Abstract

fetched live from OpenAlex

PURPOSE: According to the importance of strategic purchasing as a prerequisite for overall access and universal health coverage, this study was conducted to explore requirements and incentives for implementation of pharmaceutical strategic purchasing in the Iranian health system.METHODS: This was a qualitative study conducted through content analysis with an inductive approach applying a five-stage framework analysis. Data analysis was started right after transcribing each interview applying MAXQDA10. Data was saturated after 32 semi-structured interviews with experts. These key informants were selected purposefully and through snowball sampling.RESULTS: The findings are categorized under three main themes: “Payment Mechanisms to Service Providers”, “Insurance Reimbursement Mechanisms” and “Rules and Regulations”, and eight related subthemes.CONCLUSIONS: According to the importance of incentive interventions in pharmaceutical strategic purchasing, it is necessary to pay close attention to pharmaceutical price, realistic and fair premiums and appropriate contracts with suppliers, along with estimation a reasonable profit margin for pharmaceutical suppliers and the appropriate reimbursement mechanisms as the most significant incentives for increasing access to pharmaceuticals and implementing strategic purchasing.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.251
GPT teacher head0.510
Teacher spread0.259 · 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.

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

Citations9
Published2016
Admission routes1
Has abstractyes

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