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Record W4400042736 · doi:10.1002/hsr2.2213

Preferences of Iranian medical students for selecting the compulsory service plan packages: A discrete choice experiment

2024· article· en· W4400042736 on OpenAlexaff
Enayatollah Homaie Rad, Mohamad Hajizadeh, Mohammad Rajabpour, Zahra Mohtasham‐Amiri, Morteza Rahbar Taramsari, Faezeh Bahador, Ehsan Esmaeili Shoja

Bibliographic record

VenueHealth Science Reports · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSalaryDisadvantagedPreferenceResidenceDuration (music)Service (business)Work (physics)Medical educationIncentiveDiscrete choiceWelfarePsychologyBusinessMedicineMarketingDemographic economicsEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

Background and Aims: Health policymakers face challenges in designing compulsory plan packages for medical students to encourage them to work in disadvantaged regions. Using a discrete choice experiment, this study assessed the preferences of medical students for selecting the compulsory service plan packages in Guilan Province, Iran. Methods: In total, 374 medical students responded to a survey inquiring about salary, distance from their residency city, availability of welfare amenities, work difficulty, the developmental status of their workplace, contract duration, and preference for urban or rural work settings. Results: The study revealed that students favor a compulsory service package that provides higher salaries and shorter contract duration. They also show a preference for working within their home province over other factors. For the opportunity to serve in their city of residence, they would forgo an average of US$77.93 per month. Conclusion: While financial incentives were the primary consideration for medical students when choosing compulsory service packages, a range of nonfinancial factors significantly influenced their decisions as well.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.172
GPT teacher head0.356
Teacher spread0.184 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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