MétaCan
Menu
Back to cohort
Record W4386579402 · doi:10.18844/prosoc.v10i2.9098

The effect of Alexithymia level on communication skills in intensive care nurses

2023· article· en· W4386579402 on OpenAlexaboutno aff
İbrahim Salih Palazoğlu, Esra Danacı, Zeliha Koç

Bibliographic record

VenueNew Trends and Issues Proceedings on Humanities and Social Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaIntensive careToronto Alexithymia ScalePsychologyConformityNursingDescriptive statisticsTest (biology)Scale (ratio)Communication skillsIntensive care unitMedicineClinical psychologyMedical educationSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

This research was carried out as a descriptive study to determine the effect of alexithymia level on communication skills in intensive care nurses. The research was carried out between 15/02/2023-01/06/2023 with the participation of 105 nurses working in the intensive care units of a university hospital. The data were collected by using a 17-question information form prepared by the researcher in line with the literature, which determines the socio-demographic and communication skills of the nurses, and the Toronto Alexithymia Scale and the Health Professionals Communication Skills Scale. The conformity of the data to normal distribution was evaluated by Shapiro-Wilk and Kolmogorov Smirnov tests. Kruskal Wallis test, Mann Whitney U test, One-Way Analysis of Variance, and independent sample t-test were used in data analysis. In line with the findings obtained from this study, it was determined that intensive care nurses with high levels of alexithymia had low communication skills.
 Keywords: Alexithymia; communication; communication skills; nurse; intensive care.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.381

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)0.0000.000

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 head0.053
GPT teacher head0.358
Teacher spread0.304 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNew Trends and Issues Proceedings on Humanities and Social SciencesSame topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207