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Record W4399327279 · doi:10.1177/13674935241249597

Goals of Morbidity and Mortality meetings in paediatric acute care. A qualitative case study

2024· article· en· W4399327279 on OpenAlexaff
Emma Jeffs, Fiona Newall, Clare Delany, Sharon Kinney

Bibliographic record

VenueJournal of Child Health Care · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsQualitative researchMedicineCONTESTPsychological interventionNursingWorkforceHealth careParticipant observationMedical educationPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Morbidity and Mortality meetings are conducted in varied clinical contexts including paediatrics. Widely cited as an educational or quality improvement tool, the reality is more complex. In this qualitative study, the aim was to explore the perceived goals of the paediatric acute care Morbidity and Morbidity meeting. This study used semi-structured interviews and observation within a qualitative case study methodology. Data were collected in a large paediatric quaternary hospital. Analysis generated themes related to meeting observations and the participant's interpretation of meeting goals. A total of 44 interviews were conducted with 14 nurses, 29 doctors, and 1 allied health professional. Thirty-two meetings in six clinical departments were observed. Two themes were developed: complex and nuanced goals; and tensions and contest between and within goals. Meeting goals to evaluate care, learn, support, adhere, and change and respond were sometimes in competition and had varied interpretations. Morbidity and Mortality meetings in this setting are valued and occupy a complex role which reaches beyond identification of measurable patient safety interventions. Understanding goals more fully can lead to optimised conduct and meaningful measurement of efficacy. The strength in these meetings may be the way they promote an embedded safety culture, and an informed and skilled workforce.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.520
Teacher spread0.451 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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