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Record W2099921259 · doi:10.3109/13561820.2012.694503

Toward better care of delirious patients at the end of life: A pilot study of an interprofessional educational intervention

2012· article· en· W2099921259 on OpenAlexaffabout
Susan Brajtman, David Wright, Pippa Hall, Shirley H. Bush, Enkenyelesh Bekele

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsBruyèreUniversity of Ottawa
Fundersnot available
KeywordsCompetence (human resources)Intervention (counseling)TeamworkPsychologyInterprofessional educationNursingMedicineMedical educationHealth care

Abstract

fetched live from OpenAlex

Symptom distress with end-of-life delirium (EOLD) is complex and multidimensional, and interprofessional (IP) teams require knowledge and skill to effectively care for these patients and their families. The purpose of this pilot study was to test an educational intervention about EOLD for IP teams at a long-term care facility and a hospice. The intervention included a comprehensive self-learning module (SLM) on EOLD and IP teamwork; a modified McMaster-Ottawa team objective structured clinical encounter (TOSCE) and a didactic "theory burst" on the principles of delirium assessment, diagnosis and management. Evaluation tools completed by participants included the interprofessional collaborative competencies attainment survey (ICCAS) and the W(e) Learn. Two groups at each site participated in 1-hour sessions, repeated 2 weeks later. Only one group from each site received the SLM after the first session. Researchers scored EOLD knowledge and IP team functioning in both sessions. Results suggest that the intervention improved EOLD knowledge and perceptions of IP competence and supports the value of the TOSCE as an IP teaching method. The module does not appear responsible for the changes. Future studies are required to evaluate the effectiveness of the individual components used in this study, and to tailor the intervention to individual care contexts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.438
Teacher spread0.352 · 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.

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

Citations21
Published2012
Admission routes2
Has abstractyes

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