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Record W2054612823 · doi:10.14740/jcgo.v3i1.237

E-mail Communication in the OB/GYN Office: Analysis of Patient E-mails to Their OB/GYN

2014· article· en· W2054612823 on OpenAlexvenueno aff
Andrew L. Atkinson, Jonathan D. Baum, Meghan I. Rattigan, Debra Gussman

Bibliographic record

VenueJournal of Clinical Gynecology and Obstetrics · 2014
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical prescriptionFamily medicineObservational studyElectronic mailObstetrics and gynaecologyTest (biology)Primary carePregnancyInternal medicineNursingInternet privacy

Abstract

fetched live from OpenAlex

Background: How patients use e-mail with their obstetrician-gynecologists (ob/gyns) is unknown. E-mail was originally created as a tool for health care professionals yet physicians remain reluctant to adopt e-mail as a form of communication with patients. Many cite concerns of patient misuse, risk management issues, and uncompensated time commitment. The primary objective of this study was to examine the details of e-mail generated by patients to their ob/gyns. A secondary objective was to examine e-mails that corroborate physician concern about e-mail. Methods: A retrospective observational study was performed. E-mails from patients sent to two ob/gyns were examined. Results: Three hundred patient initiated e-mails were generated by 127 patients. Seventy-eight percent were sent during office hours and 87% of e-mails were sent on weekdays. Thirty-seven percent involved symptoms, 14% test results, 13% administrative, 12% contraception, 11% prescriptions, 8% other and 5% social. Three percent of e-mails met the definition of misuse. There were 594 follow-up e-mails exchanged from the initial 300 e-mails. Each physician received up to 6 e-mails daily. Conclusions: Patients use e-mail as an alternative to calling. More than 50% of e-mails relate to symptoms and test results. This study substantiated concerns about e-mail misuse by patients. New policies must be created to ensure that e-mail with patients is safe, effective and attractive for physicians as a form of communication with patients. J Clin Gynecol Obstet. 2014;3(1):8-13 doi: http://dx.doi.org/10.14740/jcgo232e

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)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.338
Teacher spread0.305 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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