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Record W2987187084 · doi:10.3390/ejihpe5020020

Alexithymia, resilience and paranormal beliefs in an elderly institutionalized center

2015· article· en· W2987187084 on OpenAlexaboutno aff
Inmaculada Méndez, Julia García-Sevilla, Juan Pedro Martínez-Ramón, Ana Ma Bermúdez, Pilar Pérez, Isabel García-Munuera

Bibliographic record

VenueEuropean Journal of Investigation in Health Psychology and Education · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaParanormalFeelingPsychologyToronto Alexithymia ScaleScale (ratio)Psychological resilienceResilience (materials science)Social psychologyMedicine

Abstract

fetched live from OpenAlex

Alexithymia refers to the difficulty to understand and identify feelings and those of others. The process of adapting a residential facility is one of the stressful situations older people may face. In order to overcome this situation, resilience is critical and even there are people who seek support in religion or paranormal beliefs. In this paper, we studied the relationship between alexithymia, resilience and paranormal beliefs in a group of 35 seniors (21 women) aged between 66 and 95 years old in an institutionalized center. The instruments used were the TAS-20 Scale in order to assess alexithymia, the CD-RISC for evaluating resilience and the Enhanced Paranormal Beliefs Scale. Among main findings, it must be emphasized that subjects with greater difficulty identifying feelings are those with less personal control and less belief about extraordinary life forms; subjects with greater personal control had higher belief about those extraordinary life forms. Professional applications that can be launched in elderly institutionalized centers are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.437
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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