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Impact of Interprofessional Teamwork on Aligning Intensive Care Unit Care with Patient Goals: A Qualitative Study of Transactive Memory Systems

2023· article· en· W4313639884 on OpenAlexaff
Jacqueline M. Kruser, Demetrius Solomon, Joy X. Moy, Jane L. Holl, Elizabeth M. Viglianti, Michael E. Detsky, Douglas A. Wiegmann

Bibliographic record

VenueAnnals of the American Thoracic Society · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood Institute
KeywordsTransactive memoryTeamworkMedicineQualitative researchIntensive care unitNursingIntensive care medicineKnowledge managementComputer science

Abstract

fetched live from OpenAlex

Abstract Rationale Although aligning care with patient goals is fundamental to critical care, this process is often delayed and leads to conflict among patients, families, and intensive care unit (ICU) teams. Interprofessional collaboration within ICU teams is an opportunity to improve goal-aligned care, yet this collaboration is poorly understood. A better understanding of how ICU team members work together to provide goal-aligned care may identify new strategies for improvement. Objectives Transactive memory systems is a theory of group mind that explains how high-performing teams use a shared memory and collective cognition. We applied this theory to characterize the process of interprofessional collaboration within ICU teams and its relationship with goal-aligned care. Methods We conducted a secondary analysis of focus group (n = 10) and semistructured interview (n = 8) transcripts, gathered during a parent study at two academic medical centers on the process of ICU care delivery in acute respiratory failure. Participants (N = 70) included interprofessional ICU and palliative care team members, surrogates, and patient survivors. We used directed content analysis, applying transactive memory systems theory and its major components (specialization, coordination, credibility) to examine ICU team collaboration. Results Participants described each ICU profession as having a specialized role in aligning care with patient goals. Different professions have different opportunities to gather knowledge about patient goals and priorities, which results in dispersion of this knowledge among different team members. To share and use this dispersed knowledge, ICU teams rely on an informal coordination process and “side conversations.” This process is a workaround for formal channels (e.g., health records, interprofessional rounds) that do not adequately convey knowledge about patient goals. This informal process does not occur if team members are discouraged from asserting their knowledge because of hierarchy or lack of psychological safety. Conversely, coordination succeeds when team members recognize each other as credible sources of valued knowledge. Conclusions We found that ICU team members work together to align care with patient goals and priorities, using transactive memory systems. The successful function of these systems can be disrupted or promoted by ICU organizational and cultural factors, which are potential targets for efforts to increase goal-aligned care.

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)0.027
metaresearch head score (Gemma)0.033
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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0150.016
Scholarly communication0.0050.006
Open science0.0030.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.243
GPT teacher head0.542
Teacher spread0.299 · 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".

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Citations19
Published2023
Admission routes1
Has abstractyes

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