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Record W2546010929 · doi:10.5539/gjhs.v9n6p1

Experiences of Obstetricians and Gynecologists in Teleconsultation with Medical Residents: A Qualitative Study

2016· article· en· W2546010929 on OpenAlexvenueno aff
Kolsoum Deldar, Fatemeh Tara, Masoumeh Mirteimouri, Mahmood Tara

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
FundersMashhad University of Medical Sciences
KeywordsTelephone interviewMedicineQualitative researchObstetrics and gynaecologyPopulationTelemedicineMedical educationFamily medicineNursingPsychologyPregnancyHealth care

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the experiences of specialists, residents, and experienced personnel of obstetrics and gynecology regarding telephone consultation by specialized residents and on-call expertsDESIGN: Qualitative study based on inductive content analysis.SETTING: Three departments of obstetrics and gynecology, affiliated to Mashhad University of Medical Sciences, Mashhad, Iran.POPULATION: A purposive sample of 16 specialists, residents and experienced staff.METHODS: Eighteen semi-structured interviews were conducted.RESULTS: Analysis of interview data resulted in 363 primary codes and six main themes including: “attempt to direct the process of telephone consultation”, “decision-making challenges of diagnostic-therapeutic plans for patients”, “attempt to verify the acquired findings”, “inefficacy in the face of life-threatening conditions”, “discriminations in legal confrontation with medical errors”, and “impact on emotions and personal life”.CONCLUSIONS: Process of teleconsultation between physician and resident is associated with numerous challenges. Formal training sessions and considering new approaches of teleconsultation and telemedicine are needed to be implemented in order to reinforce the reliability of patient information transfer.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.291
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.372
Teacher spread0.343 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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