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Record W2990077275 · doi:10.1136/medethics-2019-105938

Ottawa Statement does not impede randomised evaluation of government health programmes

2019· letter· en· W2990077275 on OpenAlexafffundabout
Charles Weijer, Monica Taljaard

Bibliographic record

VenueJournal of Medical Ethics · 2019
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsOttawa HospitalWestern University
FundersCanadian Institutes of Health Research
KeywordsStatement (logic)Government (linguistics)Alternative medicineMedicinePolitical scienceData scienceComputer scienceLawPathology

Abstract

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In this issue of JME , Watson et al call for research evaluation of government health programmes and identify ethical guidance, including the Ottawa Statement on the ethical design and conduct of cluster randomised trials, as a hindrance. While cluster randomised trials of health programmes as a whole should be evaluated by research ethics committees (RECs), Watson et al argue that the health programme per se is not within the researcher’s control or responsibility and, thus, is out of scope for ethics review. We argue that this view is wrong. The scope of research ethics review is not defined by researcher control or responsibility, but rather by the protection of research participants. And the randomised evaluation of health programmes impacts the liberty and welfare interests of participants insofar as they may be exposed to a harmful programme or denied access to a beneficial one. Further, Watson et al ’s claim that ‘study programmes … would occur whether or not there were any … research activities’ is incorrect in the case of cluster randomised designs. In a cluster randomised trial, the government does not implement a programme as usual. Rather, researchers collaborate with the government to randomise clusters to intervention or control conditions in order to rigorously evaluate the programme. As a result, equipoise issues are triggered that must be addressed by the REC.

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)0.076
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0760.010
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 science0.0010.000
Research integrity0.0020.006
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 head0.147
GPT teacher head0.401
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2019
Admission routes3
Has abstractyes

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