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Record W2077090865 · doi:10.1016/s0020-7292(00)00330-1

The scope and limits of conscientious objection

2000· article· en· W2077090865 on OpenAlexaff
Bernard M. Dickens, Rebecca J. Cook

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2000
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConscientious objectorOffensiveScope (computer science)Object (grammar)Health carePoint (geometry)NursingPsychologyMedicinePublic relationsLawMedical educationPolitical scienceManagementComputer scienceEconomicsSpanish Civil War

Abstract

fetched live from OpenAlex

Principles of religious freedom protect physicians, nurses and others who refuse participation in medical procedures to which they hold conscientious objections. However, they cannot decline participation in procedures to save life or continuing health. Physicians who refuse to perform procedures on religious grounds must refer their patients to non-objecting practitioners. When physicians refuse to accept applicants as patients for procedures to which they object, governmental healthcare administrators must ensure that non-objecting providers are reasonably accessible. Nurses' conscientious objections to participate directly in procedures they find religiously offensive should be accommodated, but nurses cannot object to giving patients indirect aid. Medical and nursing students cannot object to be educated about procedures in which they would not participate, but may object to having to perform them under supervision. Hospitals cannot usually claim an institutional conscientious objection, nor discriminate against potential staff applicants who would not object to participation in particular procedures.

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)0.034
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.112
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0050.065
Scholarly communication0.0110.016
Open science0.0020.012
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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 designTheoretical or conceptual
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

Citations4
Published2000
Admission routes1
Has abstractyes

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Same venueInternational Journal of Gynecology & ObstetricsSame topicReproductive Health and ContraceptionFrench-language works237,207