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Record W2321841895 · doi:10.1097/acm.0000000000000499

Failure to Cope

2014· article· en· W2321841895 on OpenAlexaffabout
Fiona Webster, Kathleen Rice, Katie N. Dainty, Merrick Zwarenstein, Steve Durant, Ayelet Kuper

Bibliographic record

VenueAcademic Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMEDLINEPsychologyMedicineMedical educationPolitical scienceLaw

Abstract

fetched live from OpenAlex

PURPOSE: The study explored optimal intraprofessional collaboration between physicians in the emergency department (ED) and those from general internal medicine (GIM). Prior to the study, a policy was initiated that mandated reductions in ED wait times. The researchers examined the impact of these changes on clinical practice and trainee education. METHOD: In 2010-2011, an ethnographic study was undertaken to observe consults between GIM and ED at an urban teaching hospital in Ontario, Canada. Additional ad hoc interviews were conducted with residents, nurses, and faculty from both departments as well as formal one-on-one interviews with 12 physicians. Data were coded and analyzed using concepts of institutional ethnography. RESULTS: Participants perceived that efficiency was more important than education and was in fact the new definition of "good" patient care. The informal label "failure to cope" to describe high-needs patients suggested that in many instances, patients were experienced as a barrier to optimal efficiency. This resulted in tension during consults as well as reduced opportunities for education. CONCLUSIONS: The authors suggest that the emphasis on wait times resulted in more importance being placed on "getting the patient out" of the ED than on providing safe, compassionate, person-centered medical care. Resource constraints were hidden within a discourse that shifted the problem of overcrowding in the ED to patients with complex chronic conditions. The term "failure to cope" became activated when overworked physicians tried to avoid assuming care for high-needs patients, masking institutionally produced stress and possibly altering the way patients are perceived.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.018
GPT teacher head0.325
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations61
Published2014
Admission routes2
Has abstractyes

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