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Record W4296498816 · doi:10.1093/pch/21.supp5.e68b

Incident Reporting as A Quality Improvement Tool in Paediatric Resuscitations

2016· article· en· W4296498816 on OpenAlexaff
S Ratnapalan, I Kelly, L Fearey

Bibliographic record

VenuePaediatrics & Child Health · 2016
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsMedicineResuscitationEmergency departmentMedical emergencyCardiopulmonary resuscitationEmergency medicineNursing

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: A code is called through locating for any child under 16 years of age who requires resuscitation in our hospital and a team of physicians, nurses and resuscitation officers attend these resuscitations. All clinicians involved in the resuscitation are sent a standardised questionnaire by the resuscitation office to identify issues during that resuscitation which may not be captured elsewhere. This feedback system was established to create a mechanism for anonymous feedback from all members of resuscitation teams. OBJECTIVES: The objective of this study was to identify the teams' perception of issues in clinical care and remedial actions that were implemented by the institution to address these gaps. DESIGN/METHODS: A retrospective review of all voluntary anonymous survey responses that were generated at our institution from January to December 2013 was conducted after Research Ethics Board review and approval. A narrative analysis was conducted by the Resuscitation Officersand a Paediatric Emergency Consultant to identify themes and categories. RESULTS: There were 56 medical and 49 trauma resuscitations occurred in 2013 of which all traumas (49) and 48 (86%) of medical resuscitations were in the emergency department There were fully or partially completed surveys available for 97 resuscitations that occurred in the emergency department during the study period. Major themes with positive comments included; the team lead from the emergency department and clear role identification during the resuscitations. Identified themes with concerns included providers’ knowledge gaps, process delays due to equipment issues and system issues. Knowledge gaps regarding drowning management, protocols for cardiopulmonary resuscitations, and lack of clarity regarding policies and procedures when a child is certified dead after resuscitation were identified. Equipment delays for paediatric resuscitation were due to absent batteries in laryngoscope, suction failure, lack of small cuffed endotracheal tubes in ED, inability to find oxygen saturation connectors, and lack of enteral syringes in resuscitation areas. The system issues that were identified included inadequate space in the resuscitation room, in-adequate number of lead aprons when there were more than one trauma case, specimen transport delays and delays in accessing paediatric sub-speciality services. CONCLUSION: Issues with resuscitations can be identified through anonymous feedback even when robust systems and processes are in place to ensure appropriate personnel and equipment are readily available at paediatric resuscitations. Our study identified provider, process and system issues in paediatric resuscitations which helped to identify strategies to improve service delivery.

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)0.143
metaresearch head score (Gemma)0.327
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.327
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0020.001
Scholarly communication0.0050.007
Open science0.0030.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.392
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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