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Record W4413780633 · doi:10.1136/bmj.r1813

When doctors lose their faith in medicine

2025· editorial· en· W4413780633 on OpenAlexaff
Peter G. Brindley, Matt Morgan

Bibliographic record

VenueBMJ · 2025
Typeeditorial
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFaithAlternative medicineMedicineWorld Wide WebData scienceFamily medicineTraditional medicineComputer sciencePathologyPhilosophyTheology

Abstract

fetched live from OpenAlex

What happens if, as a priest, vicar, rabbi, or imam, you lose your faith?Well, there is an organisation, the Clergy Project, that supports religious professionals who are questioning their convictions.1 These disenchanted professionals may no longer cherish the same rites and rituals but are supported to stay in the profession and keep helping their communities.We believe that medicine should do the same for healthcare professionals who want to carry on but are struggling to understand why.It's sad when patients lose their faith in medicine.This article, however, is about something potentially worse-a possible crisis of faith among doctors and nurses.The most recent evidence of this is a General Medical Council (GMC) survey that shows more than a third of UK doctors are dissatisfied with their careers and many are considering quitting.2 The NHS is often described as the UK's national religion, so, if you are questioning your medical vocation, we want to step up and help.

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)0.008
metaresearch head score (Gemma)0.042
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.042
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0040.004
Scholarly communication0.0080.006
Open science0.0040.002
Research integrity0.0240.027
Insufficient payload (model declined to judge)0.0080.006

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 head0.065
GPT teacher head0.482
Teacher spread0.418 · 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
GenreEditorial

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
Published2025
Admission routes1
Has abstractyes

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