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Record W2079100208 · doi:10.4236/ojmp.2014.33024

Surgery and Caregiving: Loneliness of the Patients and Those Who Care for Them

2014· article· en· W2079100208 on OpenAlexaff
Ami Rokach, Yona Miller, Sharon Shick, Rachel Abu, Idit Matot

Bibliographic record

VenueOpen Journal of Medical Psychology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsYork University
Fundersnot available
KeywordsLonelinessFeelingSocializationPsychologySignificant differenceClinical psychologyQualitative researchMedicinePsychiatryDevelopmental psychologySocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

This research, conducted on patients and caregivers, examined the qualitative aspects of their loneliness. Patients were divided into those who were approached before they had surgery, and those post operatively. We collected information about their tumors, which were either benign or malignant. The patients’ loneliness was compared to their caregivers who were either intimate partners or “others”, i.e. family members and friends. The loneliness questionnaire, has already been extensively utilized in previous studies, and was used to explore the various aspects of loneliness of those groups. Significant differences in subscale scores were found in patients pre and post surgery, with those who have already had surgery scoring higher. Additionally, those who were cared for by a partner scored lower on the loneliness subscales than those attended to by “other”. Interestingly, the only significant difference in the caregiver group was between men and women, in line with the socialization process of the genders, which makes women more open and vocal about their feelings and needs.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.500
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.115
GPT teacher head0.506
Teacher spread0.391 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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