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Vulnerabilities in clinician–parent exchanges and the cascade of communication traps: a review

2022· review· en· W4225607435 on OpenAlexaff
Emanuela Ferretti, Jordan Richard Schoenherr, Alessandra Mattiola, Thierry Daboval

Bibliographic record

VenueArchives of Disease in Childhood · 2022
Typereview
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of OttawaCarleton UniversityConcordia UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineCascade

Abstract

fetched live from OpenAlex

This review considers parent-clinician interactions that are associated with vulnerabilities in communication and what we refer to as 'communication traps'. Communication traps are defined by high-stress situations with affect-laden subject matter that can lead to progressively dysfunctional communications/exchanges that are avoidable. While this framework was developed in neonatology, it can be applied to other clinical practices.Communication competencies in paediatrics require the rapid development of a therapeutic alliance between parents and clinicians to ensure the provision of best care to their infants. In order to facilitate parent-clinician communication, our framework focuses clinicians' attention on the affective, behavioural and cognitive (ABC) cues that are indicative of real, apparent or potential communication traps. Strategies are provided to slow down clinicians' responses to more effectively consider ABC cues that suggest if patients/parents have failed to engage or disengage from a situation. This framework is illustrated by presenting a narrative synthesised from a number of experiences that clinicians have encountered. This review identifies key decision points in the communication process that, if left unaddressed, can cascade into communication traps which may be difficult to escape.Using results from communication studies and psychological research, our framework was developed to identify key decision points for ABC cues that can be used to prevent falling into communication traps.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.094
GPT teacher head0.436
Teacher spread0.342 · 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 designOther design
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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