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Record W3030164880 · doi:10.1080/14789949.2020.1772852

Independent assessors in contrast to treating physicians as expert witnesses in Canada: comparing duties and responsibilities

2020· article· en· W3030164880 on OpenAlexaffabout
J. Waldman, Tyler Oswald, E. Elizabeth Johnson, Sarah Brown

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCLARITYPsychologyLegislationExpert opinionEstatePublic relationsSocial psychologyPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

In civil and criminal proceedings, there are factors inherent in the role of a treating physician such as advocacy and other duties owed the patient, in contrast to expert opinion provided by an independent assessor which is required to be objective. Misunderstanding or unawareness of the differences in treating versus expert physicians can lead to decision-makers relying on potentially biased information. Recent decisions in Canada have allowed for opinion evidence by treatment providers (Westerhoff v. Gee Estate), which seem to ignore the potential bias of the treatment provider. The lack of clarity by which decision-makers perceive the role of physician witnesses poses a significant issue for physicians, who must balance competing interests, equivocal processes and concerns regarding bias and conflict of interest when providing medical information to a decision-maker. This paper attempts to clarify the essential differences between information provided by a treating physician as opposed to an expert opinion provided by an independent consultant in Canada. Discussion around privacy legislation will be used to highlight some differences. It is hoped that a clearer understanding of the information the decision-makers are receiving will promote an improved understanding of that information and, subsequently, an improved decision-making process.

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)0.001
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 score0.545
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.059
GPT teacher head0.414
Teacher spread0.355 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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