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Record W2761634636 · doi:10.1093/pch/9.suppl_a.52ab

110 Pediatric Postgraduate Residents' Perceptions of Ethical Dilemmas during their Training

2004· article· en· W2761634636 on OpenAlexaffabout
RI Hilliard, Christine Harrison, Shannon Madden

Bibliographic record

VenuePaediatrics & Child Health · 2004
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsThematic analysisFocus groupMedical educationCompromisePerceptionBioethicsPsychologyEthical codeQualitative researchData collectionCoding (social sciences)Ethical issuesMedicineEngineering ethicsPublic relationsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Pediatric residents face numerous ethical dilemmas. Some of the ethical issues they face are experienced by all pediatricians, and other issues are specifically related to resident training. It has been found that medical students often feel they are placed in positions which compromise their own ethical principles or feel that have been harassed or abused in their training. A US study showed that residents frequently face examples of unethical and/or unprofessional conduct amongst staff and further, the residents did not know how to respond to these issues. There is, however, little description of the ethical dilemmas facing Canadian postgraduate trainees. The purpose of this study is to identify and describe in more detail the ethical dilemmas perceived by pediatric residents in their training program. Qualitative research methods were used to study the lived experience of pediatric residents. Data collection consisted of four focus groups organized according to the four separate years of residency training. Focus groups consisted of 4–10 participants, and were led by a research assistant from the University of Toronto Joint Centre for Bioethics. The focus groups were audio taped and transcribed verbatim eliminating all data that would identify any of the participants or staff mentioned. Data analysis involved a modified thematic analysis in two steps: first in open coding and then by identifying chunks of data that relate to a concept or idea. For example, power relationships, communication issue, lack of training were three of the codes used to identify data. While residents occasionally face the traditional pediatric ethical issues such as “do not resuscitate” and “demands for futile treatment”, more often they face dilemmas due to the hierarchy of the medical care team, and these often on a daily basis. Their ability to deal and cope with these issues change as they go through their training. Many residents in the first part of their training were more frustrated and confused with ethical issues, how to address them with others or how to deal with them on their own. In these cases, residents found their best support from their peers and other senior residents. Residents in the later years of training seem more accustomed to ethical issues and did not view them quite as significant as their juniors. Common to all residents was the disconnection with senior physicians and fellows. Many felt that both staff physicians and fellows were unapproachable, over directive and in some cases accusatory. Residents at all levels of training have felt that their own moral principles have been compromised, but were unsure how to deal with this, and most dealt with it inwardly. Further, almost all residents felt that other members of their health care team have acted in an unethical or unprofessional way, and that there was no way to manage with this. Pediatric residents face serious ethical dilemmas. Understanding these ethical dilemmas will help those responsible for postgraduate medical education (a) to review or revise the ethics curriculum in keeping with the current ethical dilemmas faced by residents and (b) to mentor and guide trainees.

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)0.013
metaresearch head score (Gemma)0.035
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0020.003
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 head0.039
GPT teacher head0.348
Teacher spread0.309 · 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
Published2004
Admission routes2
Has abstractyes

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