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Record W3014180545 · doi:10.1590/1983-80422020281370

Investigating moral distress over a shortage of organs for transplantation

2020· article· en· W3014180545 on OpenAlexaffabout
João Paulo Victorino, Donna M. Wilson

Bibliographic record

VenueRevista Bioética · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEconomic shortageTransplantationPsychologyDistressTest (biology)Categorical variableAnalysis of varianceSocial psychologyClinical psychologyMedicineInternal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract We verified moral distress related to organ shortage for transplantation in nursing students. This quantitative pilot study analyzed data from 104 nursing undergraduate students. Data were collected through a survey composed of four questions and two sociodemographic items. The chi-squared test was used to examine categorical variables, whereas continuous variable data were analyzed using ANOVA and the Pearson Product Moment correlational test for determining the existence of moral distress regarding the availability of one heart for four individuals susceptible to heart transplantation. A high level of moral distress was identified with regard to the hypothetical decision-making process, which justifies the need for further studies on the subject. Given the hypothetical scenario, moral distress was observed among the students, reaching severe distress in some cases. Approval CEP-University of Alberta Pro00068610

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)0.014
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.158
GPT teacher head0.480
Teacher spread0.322 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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