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Patient and Family Engagement in Australian and New Zealand Adult Critical Care Trials

2025· article· en· W4412483212 on OpenAlexaffabout
Natalia Evertsz, Ann‐Marie Baker, Samantha Bates, Toby Betteridge, Shailesh Bihari, Heidi Buhr, Karen E. A. Burns, Marisa Comitini, Adam M. Deane, Donna Goldsmith, Richard McAllister, Sandra Peake, Lauren Phillips, DAVID B. PILCHER, Mark P. Plummer, Sumeet Rai, Mahesh Ramanan, Emma J. Ridley, Manoj Saxena, Andrew Udy, Yasmine Ali Abdelhamid, Kimberley Haines

Bibliographic record

VenueAnnals of the American Thoracic Society · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMEDLINECritical illnessIntensive care medicineFamily medicineCritically ill

Abstract

fetched live from OpenAlex

Abstract Rationale Engaging patients and families in critical care research is recognized as best practice. The extent of engagement in critical care trials in Australia and New Zealand is unknown after introduction of national guidelines in 2016. Objectives To assess the extent of patient and family engagement in adult critical care research studies endorsed by the Australian and New Zealand Intensive Care Society Clinical Trials Group (ANZICS CTG). Methods Prospective studies endorsed between January 2017 and December 2023 or previously endorsed and still recruiting during this period were included. The study design included a two-stage process: 1) retrospective independent assessment of patient and family engagement in study protocols, progress reports, and manuscripts and 2) prospective self-reported survey of study principal investigators and project managers to understand priority of engagement, types of activities, barriers, and facilitators. Both stages assessed engagement using a modified version of an existing tool developed by the Canadian Critical Care Trials Group (CCCTG) Patient and Family Partnership Committee. The Guidance for Reporting Involvement of Patients and Public 2 tool was also used in stage 1. Results Stage 1 was a retrospective analysis using the CCCTG and Guidance for Reporting Involvement of Patients and Public 2 tools. Of the 35 studies reviewed in stage 1, patient and family engagement was infrequently reported (reported in eight protocols submitted to ANZICS CTG for endorsement [8 of 35; 23%], reported in one trial progress report [1 of 34; 3%] and in one protocol publication [1 of 17; 6%], and not reported in the 10 primary trial publications [0 of 10; 0%]). Stage 2 involved survey responses using the CCCTG tool. Twenty-eight (80%) of 35 studies had at least one survey response. Respondents for 20 of these studies (20 of 28; 71%) reported undertaking some form of patient and family engagement. The most common facilitator of engagement was staff engagement experience (12 of 28; 43%), and lack of resources (12 of 28; 43%) was identified as a key barrier. Conclusions This study identified low rates of reported patient and family engagement in clinical trial protocols and manuscripts via independent appraisal compared with a high self-reported rate of engagement activities among studies endorsed via the ANZICS CTG. This study highlighted the importance of adequate resourcing for engagement activities, including experienced personnel and funding.

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)0.455
metaresearch head score (Gemma)0.579
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4550.579
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.008
Science and technology studies0.0030.004
Scholarly communication0.0080.006
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.316
GPT teacher head0.526
Teacher spread0.210 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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