MétaCan
Menu
← Back to cohort
Record W4307804959 · doi:10.1210/jendso/bvac150.711

ODP579 An EMR And Educational Intervention To Avoid Excessive Fingerstick Blood Glucose Testing In Low-risk Hospitalized Patients With Type Ii Diabetes

2022· article· en· W4307804959 on OpenAlexaboutno aff
Yiqiao Wang, Omar Saeed, Diana Dolmans, Catherine Yu

Bibliographic record

VenueJournal of the Endocrine Society · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineMedicineQualitative researchTelehealthPandemicFamily medicineHealth careNursingCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Introduction Telemedicine has become the cornerstone of health care delivery in the COVID-19 pandemic, as it allows patients to be cared for at a distance. Though institutions have published experiences with telemedicine incorporated into residency training programs during the pandemic, there is a lack of qualitative data in this field. This descriptive qualitative study sought to (1) explore internal medicine resident experiences with telehealth patient encounters during the COVID-19 pandemic, and (2) understand resident experiences with virtually reviewing cases with their supervisors. Methods From November 2020 to March 2021, the authors conducted 20 semi-structured interviews with internal medicine residents at the University of Toronto. Residents were included if they completed ambulatory rotations such as endocrinology. Interviews were transcribed, line-by-line coding was done in parallel, and themes were derived through inductive and constant comparative analysis. Results Resident experiences with telemedicine were divided into themes according to the perceived benefits and challenges with patient encounters, and with virtually reviewing cases with supervisors. Perceived benefits in patient encounters included less pressure for time and increased efficiency, and residents looked at this patient care modality as an important component of their future careers. Challenges included a deficiency of nonverbal cues to use for building rapport with patients and confirming their understanding, inability to confidently form an impression of a patient and their disease severity, the lack of physical examination, and technical audio-visual challenges. While most residents preferred in-person to virtual review with their supervisors, the benefits of the virtual review included a supportive learning environment, and a high level of autonomy for senior residents. However, residents felt that feedback over a virtual platform was generic and not constructive, and junior trainees did not have opportunities to observe staff demonstrate essential skills needed for telemedicine care. Discussion Telemedicine incorporation into postgraduate medical education is still in its early stages, and data on resident experiences is crucial to inform ways of optimizing the learning environment and preceptorship of such encounters. The virtual review process, limited by the same communication challenges that residents faced with patients, inherently involves less supervision and guidance than an in-person review. While this may support the learning needs of more experienced residents by offering more autonomy, junior residents may be affected by the lack of opportunities for observation of essential skills and the absence of coaching through constructive feedback. More research is needed especially on educational interventions that can help overcome the virtual communication challenges between residents, patients, and their clinical supervisors. Presentation: No date and time listed

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.290
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Endocrine Society→Same topicTelemedicine and Telehealth Implementation→French-language works237,207→