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Record W2767866016 · doi:10.1177/1477750917738109

(Mis)understandings and uses of ‘culture’ in bioethics deliberations over parental refusal of treatment: Children with cancer

2017· article· en· W2767866016 on OpenAlexaffabout
Ben Gray, Fern Brunger

Bibliographic record

VenueClinical Ethics · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBioethicsCompetence (human resources)Ethnic groupMedicinePsychologyLawSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

We developed this study to examine the issue of parental refusal of treatment, looking at the issue through a cultural competence lens. Recent cases in Canada where courts have declined applications by clinicians for court orders to overrule parental refusal of treatment highlight the dispute in this area. This study analyses the 16 cases of a larger group of 24 cases that were selected by a literature review where cultural or religious beliefs or ethnic identity was described as important reasons behind the refusal. The most significant finding was that nearly all of the cases cited unacceptable side effects as the main reason for declining treatment. We then analysed the detail of the cases and concluded that in the first instance a skilled clinical approach to develop an agreed management plan is by far the best approach. In the event that agreement cannot be reached we recommend engaging a mediator to help the clinician and parents/child to find an agreeable way forward. We argue that the option of seeking a court order was of significant detriment to many of the children in our cases and that this option should be used sparingly. There is a need for empirical research on the outcome of cases where a court order is sought.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.313
GPT teacher head0.551
Teacher spread0.238 · 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 designObservational
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

Citations7
Published2017
Admission routes2
Has abstractyes

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