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Record W7162137394 · doi:10.82308/7851

Understanding the effectiveness of informed consent in pediatric surgery

2022· dissertation· en· W7162137394 on OpenAlexaboutno aff
Zoe Atsaidis

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInformed consentConversationPediatric surgeryStrengths and weaknessesBest practiceParental consentMEDLINE

Abstract

fetched live from OpenAlex

Background: The consent conversation is an essential part of the pre-operative decision-making process. The manner in which this consent conversation is led has significant consequences, yet the process is imperfect. We aim to improve the teaching of effective consenting processes in pediatric surgery by identifying and sharing evidence based on the literature and a clinical study. Ultimately, our research addresses the knowledge gap surrounding the effectiveness of informed consent and emphasizes the importance of the patient perspective in the process. Methods: The first phase of this project is a systematic literature review identifying the best practices of informed consent, followed by two clinical phases that involve pediatric surgeons at the Montreal Children's Hospital. The second phase consisted of interviews in which the surgeons will be asked to consent a standardized parent for their child's surgery. The videos were filmed and evaluated using a questionnaire by patient’s and families of various medical and surgical backgrounds.Results: Our research has identified strengths and weaknesses of the current informed consent process in pediatric surgery. Aspects of the process that have been found to be effective include the use of multimedia, adequate time, surgeon empathy, the possibility of multiple conversations, and adopting an individualized shared decision-making approach. Some areas of the process that may need improvement include better use of language by the surgeon, more time for questions, recognition of parental anxiety and improvement of recall, and consideration of the child and their rights. Conclusions: Our results highlight potential areas for improvement in the current process. Upon completion of this work, we hope to compile the information from the clinical study and the literature concerning effective consent processes and use it to create new consenting videos that can be disseminated as a teaching resource for medical students and surgical residents

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)0.562
metaresearch head score (Gemma)0.707
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score0.541

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5620.707
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.005
Science and technology studies0.0080.055
Scholarly communication0.0170.039
Open science0.0050.012
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0020.000

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 head0.426
GPT teacher head0.472
Teacher spread0.046 · 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.

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
Published2022
Admission routes1
Has abstractyes

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