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Are Nomophobia and Alexithymia Related? The Case of Health Students

2022· article· en· W4220774723 on OpenAlexaboutno aff
Fatma Genç, Çağla Yığıtbaş

Bibliographic record

VenueClinical and Experimental Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScaleMedicineClinical psychologyPsychology

Abstract

fetched live from OpenAlex

Objective: This paper aims to determine whether there is a relationship between nomophobia and alexithymia in nursing and midwiferystudents studying at the undergraduate level and the factors affecting nomophobia and alexithymia.Method: This cross-sectional study was conducted with undergraduate nursing and midwifery students in a public university. No sampling wasused. The response rate was 71.42%. Data were collected by a Personal Information Form, Nomophobia Scale, and Toronto Alexithymia Scale.The data were analyzed with the SPSS-22 program. Type 1 error level was considered as p<0.05.Results: The nomophobia scores of female students, third-year students, and those who spent most of their lives in urban areas were higherand statistically significant. The Toronto Alexithymia Scale-20 scores of nursing students (p=0.022) and students with chronic diseases (p=0.011)were higher and statistically significant. There is a very weak positive correlation between the duration of daily telephone usage and nomophobia(p<0.01). In addition, a weak level positive correlation was found between nomophobia and alexithymia scores (p<0.01).Conclusion: The participants’ nomophobia scores were at a moderate level. The mean of the scores obtained by the participants from thealexithymia scale was close to half of the mean score to be taken from the scale. No significant difference was found between many sociodemographiccharacteristics and nomophobia groupings (low, moderate, severe).

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.095
GPT teacher head0.475
Teacher spread0.380 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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