MétaCan
Menu
Back to cohort
Record W1597968704 · doi:10.1080/21507740.2015.1047053

Improving Empathy in the Care of Pain Patients

2015· article· en· W1597968704 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAJOB Neuroscience · 2015
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsEmpathyPsychologyPsychotherapistPsychiatryPsychoanalysis

Abstract

fetched live from OpenAlex

Empathy is associated with countless benefits in clinical interactions, yet it is not always optimal in health care providers. Social neuroscience offers a window onto the cerebral processes underlying the complex relationships between the multiple components of empathy, patient care, and the caregiver's well-being. Neuroimaging studies have revealed patterns of empathy-related neural responses that shed some light on the mechanisms that could partially explain the phenomena of empathy decline and pain underestimation in health care providers. Such information, complementary to behavioral research findings, may help develop new means of improving empathy in health care, as long as interpretation of neuroimaging data remains grounded. Additionally, research on empathy in this context has largely focused on how clinicians' empathy may affect patient outcomes, but the relationship between empathy and well-being in health care providers is often neglected. The quest to optimize empathy in patient–clinician interactions must take into account the welfare of both members of this dyad.

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.

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)0.004
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 score0.419
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0000.000
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 head0.031
GPT teacher head0.301
Teacher spread0.269 · 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