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Record W4306666302 · doi:10.11591/ijphs.v11i4.21890

Interpersonal mattering and students’ friendship quality as predictors of subjective wellbeing

2022· article· en· W4306666302 on OpenAlexaboutno aff
Kylie Kai Ni Yap, Kususanto Ditto Prihadi, Susanna Poay Lin Hong, Fahyuni Baharuddin

Bibliographic record

VenueInternational Journal of Public Health Science (IJPHS) · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsFriendshipHappinessPsychologySubjective well-beingInterpersonal relationshipInterpersonal communicationSocial psychologyNonprobability samplingQuality (philosophy)Developmental psychologyDemography

Abstract

fetched live from OpenAlex

<p>Subjective wellbeing (SWB) refers to one’s subjective assessment of happiness. Studies reported that happiness or SWB is predicted by friendship quality. However, others reported that SWB is strongly predicted by the sense that we matter to others (interpersonal mattering). This non-experimental correlational study aimed to test the hypothesis whether interpersonal mattering is a better predictor of SWB than friendship quality. One-hundred-and-nineteen emerging adults were recruited through convenience-purposive sampling with inclusion criteria includes Malaysian within 18 to 25 years of age. The sample size was gotten through G*Power calculator with .15 effect size, .95 Power, and .05 alpha level. The participants were asked to fill up the mcgill friendship questionnaire-friend’s functions (MFQFF), mattering to others questionnaire (MTOQ), and subjective happiness scale (SHS). We tested the hypotheses that while both friendship quality and interpersonal mattering predict SWB, the latter was the strongest predictor. Results of the multiple regression analyses showed that individuals who feels they matter to others might have higher SWB.</p>

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.453
Teacher spread0.367 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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