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Record W3113770040 · doi:10.3389/fped.2020.598475

Assessment of Healthcare Provider Workload in Neonatal Resuscitation

2020· article· en· W3113770040 on OpenAlexafffundabout
Emily Zehnder, Brenda Hiu Yan Law, Georg M. Schmölzer

Bibliographic record

VenueFrontiers in Pediatrics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersUniversity of AlbertaChildren's Hospital FoundationStollery Children’s Hospital FoundationWomen and Children's Health Research InstituteChildren's Health Research InstituteHeart and Stroke Foundation of Canada
KeywordsWorkloadNeonatal resuscitationMedicinePsychological interventionInterquartile rangeNeonatal intensive care unitHealth careIntensive careGestational ageEmergency medicineMedical emergencyResuscitationPediatricsNursingPregnancyIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Objective: Human errors or protocol deviations during neonatal resuscitation are common. Excess workload has been proposed as a contributor to human error during medical tasks. We aim to characterize healthcare providers' perceived workload during neonatal resuscitation. Design: Perceived workload was measured using a multi-dimensional retrospective National Aeronautics and Space Administration Task Load Index (NASA TLX) survey. The NASA TLX collects data on mental, physical, and temporal demand, performance, effort, and frustration. Each section is rated independently by participants on a scale of 0–20 (0 being lowest and 20 being highest). The Raw-TLX score is a composite score of all dimensions and presented on a scale of 0–100. Healthcare providers complete a paper and pencil survey after attending delivery room resuscitations within 3 months. Setting: Level three neonatal intensive care unit at the Royal Alexandra Hospital, Edmonton, AB, Canada. Participants: All neonatal healthcare providers who attended deliveries. Exposure: Participation in the delivery room care of newborns. Measurement: Raw TLX scores as a measure of overall workload and scores for each dimension of workload. Main Results: During the study period, ~880 neonatal resuscitation events occurred, and a total of 204 surveys were completed. Healthcare providers completed one survey for 179 deliveries, two surveys for 20 deliveries, and three surveys for 5 deliveries. The mean (standard deviation) gestational age was 35 (5) weeks, and the median (interquartile range) birth weight was 2,690 (1,830–3,440) g. Interventions at delivery were (i) stimulation 149 (73%), suction 130 (64%), continuous positive airway pressure 120 (59%), positive pressure ventilation 105 (52%), intubation 33 (16%), chest compression 10 (5%), and epinephrine 4 (2%). The overall median (interquartile range) Raw-TLX was 34 (18–49). The scores varied by dimension with mental demand 10 (5–14), physical demand 4 (1–6), temporal demand 8 (3–14), performance 4 (2–6), effort 8 (4–13), and frustration 4 (1–10). Raw-TLX scores were higher when healthcare providers performed any intervention compared to no intervention [35 (22–49) vs. 8 (6–18), p = 0.0011]; intubation and no intubation was [55 (46–62) vs. 30 (17–46), p = 0.0001], and between performing chest compression vs. no chest compression [55 (49–64) vs. 33 (18–47), p = 0.001]. Conclusion: Perceived workload of neonatal healthcare providers increases during higher acuity deliveries. Healthcare providers' workload during neonatal resuscitation can be measured using NASATLX and was inversely associated with 5-min Apgar score. Future studies assessing healthcare providers' perceived workload during neonatal resuscitation in different settings are warranted.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.394
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.397
Teacher spread0.347 · 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

Citations20
Published2020
Admission routes3
Has abstractyes

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