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Record W4399921685 · doi:10.26443/ijwpc.v11i2.448

Narrative Medicine: Reuniting our sense of purpose as clinicians and protecting against depersonalization and burnout

2024· article· en· W4399921685 on OpenAlexaffvenue
Charlene Habibi

Bibliographic record

VenueInternational Journal of Whole Person Care · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsDepersonalizationBurnoutNarrativePsychologyNarrative medicineMedicineEmotional exhaustionPsychotherapistClinical psychologyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

ost western-trained medical students have encountered at least one PowerPoint slide early on in their training quoting Hippocrates, Father of Medicine, proclaiming that, "Where the art of medicine is loved, there is also love for humanity".Often met with a deep sense of awe and pride, this sentiment serves as inspiration to trainees worldwide as they embark on the life-long process of strengthening the knowledge, attitude and skills needed for this work: that of healing.Medical students, staff and resident physicians enter this field, one of implicit personal sacrifice made in favor of countless hours spent studying in libraries or at the bedside caring for ailing patients, with the hope that it will have been worthwhile.It is thus rarely primarily financial prosperity, rank, or status that drive prospective applicants to this discipline, but rather the desire to selflessly help our fellow humans in times of vulnerability and need.[1] In this way, the practice of medicine is widely regarded as beyond that of a career or a vocation, but one of calling. THE ONSET OF BURNOUTFast-forward a few years into residency or independent practice, and far too often little is left of this initial love for the art of medicine, let alone that for humanity.Hundreds, if not thousands, of sleepless nights later -sprinkled with unrelenting and, at times grueling, constructive feedback all whilst juggling emotionally

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.027
GPT teacher head0.372
Teacher spread0.345 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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