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Record W4404231752 · doi:10.2196/54841

Assessing the Quality of an Online Democratic Deliberation on COVID-19 Pandemic Triage Protocols for Access to Critical Care in an Extreme Pandemic Context: Mixed Methods Study

2024· article· en· W4404231752 on OpenAlexafffundvenueabout
CLAUDIA RAMIREZ, Yanick Farmer, James Downar, Andrea Frolic, Lucie Opatrny, Diane Poirier, Gina Bravo, Audrey L’Espérance, Nathalie Gaucher, Antoine Payot, Joseph Dahine, Peter Tanuseputro, Louis-Martin Rousseau, Vincent Dumez, Annie Descôteaux, Clara Dallaire, Karell Laporte, Marie-Éve Bouthillier

Bibliographic record

VenueJournal of Participatory Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsPolytechnique MontréalÉcole Nationale d'Administration PubliqueCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHamilton Health SciencesUniversity of OttawaCentre Hospitalier Universitaire Sainte-JustineUniversité de SherbrookeUniversité du Québec à MontréalMcGill University Health CentreUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsPandemicDeliberationContext (archaeology)Coronavirus disease 2019 (COVID-19)Triage2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Quality (philosophy)Political scienceMedicineMedical emergencyVirologyOutbreakGeographyPolitics

Abstract

fetched live from OpenAlex

BACKGROUND: Online democratic deliberation (ODD) may foster public engagement in new health strategies by providing opportunities for knowledge exchange between experts, policy makers, and the public. It can favor decision-making by generating new points of view and solutions to existing problems. Deliberation experts recommend gathering feedback from participants to optimize future implementation. However, this online modality has not been frequently evaluated. OBJECTIVE: This study aims to (1) assess the quality of an ODD held in Quebec and Ontario, Canada, on the topic of COVID-19 triage protocols for access to critical care in an extreme pandemic context and (2) determine its transformative aspect according to the perceptions of participants. METHODS: We conducted a simultaneous ODD in Quebec and Ontario on May 28 and June 4, 2022, with a diversified target audience not working in the health care system. We used a thematic analysis for the transcripts of the deliberation and the written comments of the participants related to the quality of the process. Participants responded to a postdeliberation questionnaire to assess the quality of the ODD and identify changes in their perspectives on COVID-19 pandemic triage protocols after the deliberation exercise. Descriptive statistics were used. An index was calculated to determine equality of participation. RESULTS: The ODD involved 47 diverse participants from the public (n=20, 43% from Quebec and n=27, 57% from Ontario). Five themes emerged: (1) process appreciation, (2) learning experience, (3) reflecting on the common good, (4) technological aspects, and (5) transformative aspects. A total of 46 participants responded to the questionnaire. Participants considered the quality of the ODD satisfactory in terms of process, information shared, reasoning, and videoconferencing. A total of 4 (80%) of 5 participants reported at least 1 change of perspective on some of the criteria and values discussed. Most participants reported that the online modality was accessible and user-friendly. We found low polarization when calculating equal participation. Improvements identified were measures to replace participants when unable to connect and optimization of time during discussions. CONCLUSIONS: Overall, the participants perceived the quality of ODD as satisfactory. Some participants self-reported a change of opinion after deliberation. The online modality may be an acceptable alternative for democratic deliberation but with some organizational adaptations.

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)0.023
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.810
GPT teacher head0.715
Teacher spread0.095 · 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.

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

Citations1
Published2024
Admission routes4
Has abstractyes

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