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Record W4382794274 · doi:10.1016/j.chstcc.2023.100009

Importance of Reconnection With ICU Survivors to ICU Recovery Program Clinicians

2023· article· en· W4382794274 on OpenAlexaboutno aff
Tammy L. Eaton, Valerie Danesh, Carla M. Sevin, Kelly Potter, Han Su, Theodore J. Iwashyna, Leanne M. Boehm, Joanne McPeake, Taylor Bernstein, Rita N. Bakhru, Michael Baram, Michelle Biehl, Amy L. Bellinghausen, J. Gordon Boyd, Brad W. Butcher, Melanie Dalton, Neha Dangayach, K. Sarah Hoehn, Aluko A. Hope, David Hornstein, Sugeet Jagpal, Sarah E. Jolley, Babar Khan, Michael T. Kenes, Janet A. Kloos, Karen Korzick, Lindsay Lief, Eric J. Mahoney, Jason H. Maley, Kirby P. Mayer, Tresa McNeal, Jakob I. McSparron, Joel Meyer, Rima A. Mohammad, Ashley Montgomery-Yates, Vanessa Nomellini, Ann M. Parker, Kehllee Popovich, Janelle Poyant, Tara Quasim, Howard L. Saft, Lekshmi Santhosh, Kristin Schwab, Andrew Slack, Joanna L. Stollings, David Cordeiro Sousa, Heather Torbic, Thomas S. Valley, Darío Villalba

Bibliographic record

VenueCHEST Critical Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteVanderbilt University Medical CenterUniversity of PittsburghJohns Hopkins UniversityUniversity of MichiganVanderbilt UniversityAmerican College of NeuropsychopharmacologyMichigan Medicine, University of Michigan
KeywordsScopusBurnoutWorkforceSummitPsychological interventionMedicineNursingHealth careIntensive careMEDLINEPsychologyPolitical scienceIntensive care medicineClinical psychology

Abstract

fetched live from OpenAlex

Presently, ICU recovery care has appropriately focused on ICU survivor and caregiver outcomes. The provision of ICU recovery services through specialized post-ICU programs are one approach clinicians and researchers have focused their efforts on to improve outcomes.1 However, the impact of these ICU recovery programs on other parts of health care delivery, specifically workforce well-being, are unknown. Addressing clinician well-being and burnout has been a major priority of leading critical care societies, health care systems, and governments since 2014.

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)0.003
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.136
GPT teacher head0.457
Teacher spread0.321 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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