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Record W3209997614 · doi:10.1097/cce.0000000000000562

A Modified Delphi Process to Prioritize Experiences and Guidance Related to ICU Restricted Visitation Policies During the Coronavirus Disease 2019 Pandemic

2021· article· en· W3209997614 on OpenAlexafffundabout
Kirsten M. Fiest, Karla D. Krewulak, Kira Makuk, Natalia Jaworska, Laura Hernández, Sean M. Bagshaw, Karen E. A. Burns, Christopher J. Doig, Alison Fox‐Robichaud, Robert Fowler, Michelle E. Kho, Ken Kuljit S. Parhar, Oleksa Rewa, Bram Rochwerg, Bonnie G. Sept, Andrea Soo, Sean A. Spence, Andrew West, Henry T. Stelfox, Jeanna Parsons Leigh

Bibliographic record

VenueCritical Care Explorations · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsSunnybrook Health Science CentreDalhousie UniversityHamilton Health SciencesUniversity of TorontoSt. Michael's HospitalAlberta HealthUniversity of AlbertaImpactUniversity of CalgaryMcMaster UniversityAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CoronavirusDiseaseProcess (computing)MedicineVirologyIntensive care medicineComputer scienceInfectious disease (medical specialty)OutbreakInternal medicine

Abstract

fetched live from OpenAlex

To create evidence-based consensus statements for restricted ICU visitation policies to support critically ill patients, families, and healthcare professionals during current and future pandemics. DESIGN: Three rounds of a remote modified Delphi consensus process. SETTING: Online survey and virtual polling from February 2, 2021, to April 8, 2021. SUBJECTS: Stakeholders (patients, families, clinicians, researchers, allied health professionals, decision-makers) admitted to or working in Canadian ICUs during the coronavirus disease 2019 pandemic. MEASUREMENTS AND MAIN RESULTS: During Round 1, key stakeholders used a 9-point Likert scale to rate experiences (1-not significant, 9-significant impact on patients, families, healthcare professionals, or patient- and family-centered care) and strategies (1-not essential, 9-essential recommendation for inclusion in the development of restricted visitation policies) and used a free-text box to capture experiences/strategies we may have missed. Consensus was achieved if the median score was 7-9 or 1-3. During Round 2, participants used a 9-point Likert scale to re-rate experiences/strategies that did not meet consensus during Round 1 (median score of 4-6) and rate new items identified in Round 1. During Rounds 2 and 3, participants ranked items that reached consensus by order of importance (relative to other related items and experiences) using a weighted ranking system (0-100 points). Participants prioritized 11 experiences (e.g., variability of family's comfort with technology, healthcare professional moral distress) and developed 21 consensus statements (e.g., communicate policy changes to the hospital staff before the public, permit visitors at end-of-life regardless of coronavirus disease 2019 status, creating a clear definition for end-of-life) regarding restricted visitation policies. CONCLUSIONS: We have formulated evidence-informed consensus statements regarding restricted visitation policies informed by diverse stakeholders, which could enhance patient- and family-centered care during a pandemic.

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)0.165
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.165
Threshold uncertainty score0.875

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1650.128
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0060.005
Science and technology studies0.0060.005
Scholarly communication0.0040.005
Open science0.0030.013
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0140.003

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 head0.141
GPT teacher head0.466
Teacher spread0.325 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes3
Has abstractyes

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