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Record W2794633028 · doi:10.7759/cureus.2399

The Acute Care of Chronic Pain Study: Perceptions of Acute Care Providers on Chronic Pain, a Social Media-based Investigation

2018· article· en· W2794633028 on OpenAlexafffund
Eric Chen, Daniel Tsoy, Suneel Upadhye, Teresa M. Chan

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsMedicineChronic painEmergency departmentAcute careEtiologyAcute painPhysical therapyHealth careNursingPsychiatryAnesthesia

Abstract

fetched live from OpenAlex

Introduction The diagnosis of chronic pain involves symptoms of pain of various etiologies lasting longer than six months. The prevalence of chronic pain in society ranges from 19% to 31% in North America. While chronic pain patient perceptions on the care provided to them in the Emergency Department (ED) have been studied, there has not been significant attention given to the attitudes of acute care providers towards these patients. Methods We utilized online questionnaires disseminated on Twitter, Facebook, Reddit, and emergency medicine blogs to gauge care provider attitudes of chronic pain patients. Survey respondents included ED physicians and their trainees, ED nurses and nurse practitioners, paramedics, and physician assistants. Results Responses revealed numerous factors impacting care provider dissatisfaction with treating chronic pain in the ED; significant factors included the lack of longitudinal care and inappropriate medication of chronic pain resulting in dependency. We found that additional chronic pain-specific training was associated with increased care provider confidence in the treatment of chronic pain. Practice patterns were found to be varied, with half of the respondents stating that chronic pain should be medicated acutely. Conclusions We conclude that acute care provider dissatisfaction with chronic pain treatment is multifactorial in origin and that confidence in the acute treatment of chronic pain can be improved with chronic pain-specific training.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.299
Teacher spread0.286 · 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 designQualitative
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

Citations11
Published2018
Admission routes2
Has abstractyes

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