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Improved health or improved decision making? The ethical goals of EBM

2011· article· en· W1539785346 on OpenAlexaff
Mona Gupta

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité de MontréalHôpital Saint-Luc
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Informed consentEvidence-based medicinePsychologyValue (mathematics)Engineering ethicsMedicineMedical educationMEDLINEPublic relationsNursingAlternative medicinePolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Evidence-based medicine (EBM) is frequently portrayed as a value-free approach to knowing what kinds of treatment 'really work.' Since practitioners should help patients to improve their health, and EBM tells us which interventions will work, then it follows that we must practice EBM, offering only those interventions supported by evidence. The primary goal of EBM, then, is an ethical one - to improve health. More recently, EBM's authors have also committed themselves to 'shared decision making' in which evidence plays a role in the clinical encounter, but where patients, motivated by their own values, should have final decision-making authority. Envisioned this way, strengthening the informed consent process, rather than improved health per se, is viewed as the goal of EBM. In this paper, I will explore this shift in EBM's ethics from the goal of improved health towards the goal of strengthened informed consent. Drawing upon data from a qualitative enquiry of scholars involved in the development of EBM, I will argue that EBM is now committed to both of these ethical goals. Where they conflict, the aim of the intervention will determine which goal practitioners should pursue. Having increased the ethical complexity of EBM, we are left with the question of whether EBM would still be judged a success if it did not lead to much in the way of improvements in health, but primarily strengthened informed consent. This paper will conclude by arguing that this more nuanced version of EBM's ethics accurately reflects the dynamics of real clinical practice but undermines the original, perceived need for EBM.

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)0.066
metaresearch head score (Gemma)0.287
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0660.287
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0010.000

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 head0.610
GPT teacher head0.648
Teacher spread0.037 · 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; both teacher heads agree on what is shown here.

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

Citations36
Published2011
Admission routes1
Has abstractyes

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