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Record W3004295200 · doi:10.1080/0142159x.2019.1708292

Cost-consciousness among Iranian internal medicine residents

2020· article· en· W3004295200 on OpenAlexaboutno aff
Nastaran Maghbouli, Ali Akbari Sari, Fariba Asghari

Bibliographic record

VenueMedical Teacher · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEstimationHealth careFamily medicineQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Background: Study aimed at assessing residents' cost awareness and their attitude about health care costs.Methods: Internal medicine residents at teaching hospitals of Tehran University of Medical Sciences were surveyed during August–December 2016 using a researcher-made questionnaire comprising attitude statements and cost estimation of diagnostic and treatment items.Results: Eighty-nine residents completed the survey (response rate = 56.6%). The results indicate that less than one quarter (23.69%) of cost estimates were in the range of correct answers. The mean (SD) for correct estimation of medications (out of 8 scores), lab tests (out of 20 scores), and total (out of 35 scores) were 1.25 (0.96), 4.92 (0.27), and 7.97 (0.34), respectively. An analysis of variance showed that the level of residency was positively correlated with residents’ correct cost estimation (F (3, 77)=9.98, p = 0.029). There was a significant positive correlation between age of residents with the correct estimate of medication prices (p = 0.018, r = 0.261).Conclusions: The internal medicine residents of Tehran University of Medical Sciences have poor knowledge of health care costs, including medications, diagnostic tests, and hospitalization costs. The results of this study explain the necessity of developing a training program for the transfer of cost information to physicians.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0140.001

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 head0.110
GPT teacher head0.320
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2020
Admission routes1
Has abstractyes

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