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Conscience in Reproductive Health Care

2020· book· en· W4239306395 on OpenAlexaff
Carolyn McLeod

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsConscienceReproductive healthNursingPsychologyMedicinePolitical scienceEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Abstract There is a growing trend worldwide of health care professionals conscientiously refusing to provide abortions and similar reproductive health services in countries where these services are legal and professionally accepted. Carolyn McLeod responds to this problem by arguing that conscientious objectors in health care should have to prioritize the interests of patients in receiving care over their own interest in acting on their conscience. She defends this “prioritizing approach” to conscientious objection over the more popular “compromise approach” in bioethics. All the while, she is careful not to downplay the importance of health care professionals having a conscience or the moral complexity of their conscientious refusals. McLeod first describes what is at stake for the main parties to the conflicts generated by conscientious refusals in reproductive health care: the objector and the patient. She then defends the prioritizing approach to these refusals. Her central argument is that health care professionals who are charged with gatekeeping access to services like abortions are normatively fiduciaries for both their patients and the public they are licensed to serve. As such, they have a duty of loyalty to these beneficiaries and must give primacy to their interests in gaining access to care. The insights contained in the book extend beyond the ethics of conscientious refusals to other topics in ethics including the value of conscience and the fundamental moral nature of the relationships health care professionals have with current and prospective patients.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.168
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.344
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations26
Published2020
Admission routes1
Has abstractyes

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