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Record W4395027212 · doi:10.1007/978-3-031-41804-4_5

Adapting and Adaptive Research

2024· book-chapter· en· W4395027212 on OpenAlexaff
Maxwell J. Smith

Bibliographic record

VenuePublic health ethics analysis · 2024
Typebook-chapter
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Abstract Research conducted during epidemics may warrant adaptations or adaptive designs owing to practical constraints, time pressures, uncertainty, the importance of flexibility, and the potential for research to detract from epidemic response. Adapting research entails choosing different research designs or methods if research goals, contexts or constraints justify or require a different approach. Adaptive research, by contrast, is a type of research that prospectively plans for modifications after research has been initiated, while maintaining the validity and integrity of the research. While adaptation and adaptive designs introduce an important degree of flexibility to research conducted during epidemics and help to address research objectives and constraints, adaptation and adaptive designs require close ethical scrutiny and are no different from other research in that they are expected to align with universally accepted ethical standards. Important ethical questions exist regarding the conditions that justify adaptations to research, the kinds of adaptive research designs that can be ethically justified, and how ethics review bodies ought to evaluate such novel approaches to research in epidemic contexts. The five cases included in this chapter prompt reflection on the ethical considerations and implications of adapting research in response to epidemic-related risks and the public health measures deployed in response to those risks, as well as the ethical implications of not adapting research in such contexts. These cases also highlight ethical questions and issues arising during the conduct of adaptive trials, including when treatments under study, treatment doses, sample size, and other study features are reviewed in response to evolving evidence. This chapter invites reflection on these key ethical dimensions when considering adaptive designs and adaptations to standard research procedures during epidemics. What these cases make clear is that adaptive designs and adaptations to research do not reduce the need for rigorous scientific evaluation and adherence to universal ethical standards, and must be explicitly ethically justified and reviewed through transparent and inclusive processes.

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)0.400
metaresearch head score (Gemma)0.449
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.400
Threshold uncertainty score0.740

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4000.449
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0070.067
Scholarly communication0.0180.023
Open science0.0060.016
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0070.002

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 head0.924
GPT teacher head0.694
Teacher spread0.230 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2024
Admission routes1
Has abstractyes

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