MétaCan
Menu
Back to cohort

Improving interprofessional handover on labor and delivery: A needs assessment study

2021· article· en· W6901883569 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHandoverFeelingProcess (computing)Scope (computer science)Situation awarenessUnit (ring theory)Needs assessmentPerception

Abstract

fetched live from OpenAlex

Handover is the transfer of important clinical information between health providers. The current report describes a needs assessment of interprofessional labor and delivery handover at an urban hospital in Canada. The goal of this study was to explore the perceptions of the current handover meeting and opportunities for improvement. Using a constructivist paradigm, we conducted 28 semi-structured inter- views with handover participants. We used a recruitment grid to ensure we included the voices of participants representing each profession involved in interprofessional handover meetings. An inductive process was used to code the interview transcripts and theme the data. Major themes identified were: (1) Interprofessional handover contributes positively to team situational awareness, interprofessional relationships, and team communication; (2) Handover could be better if it had a more defined process; (3) Interprofessional handover can lead to feelings of intimidation; and (4) Interprofessional handovers on the labor and delivery unit in our setting need increased inclusivity of midwives. From these themes, continued development of interprofessional handover meetings on labor and delivery should be aimed at a formal definition of the scope and process for these meetings reducing feelings of intimidation, increased integration of Midwifery providers, and continued improvement of relationships between different professions and practitioners on labor and 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0800.000

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 head0.023
GPT teacher head0.323
Teacher spread0.300 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicHospital Admissions and OutcomesFrench-language works237,207