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Psychological Resource as a Necessary Condition for Students’ Mental Health and Study Adjustment

2024· article· en· W4400441226 on OpenAlexaff
Ming Li, Michał Wilczewski, Paola Giuri, Zhixi Cecilia Zhuang

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMental healthResource (disambiguation)PsychologyApplied psychologyClinical psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Drawing on the conservation of resource (COR) theory, this study examines individual psychological resource as a necessary condition for students’ mental health and study adjustment during the Covid-19 pandemic. We further examine how international and domestic students differ in their resources, mental health and study adjustment. Employing partial least squares structural equation modeling (PLS-SEM), the study tests the hypothesized effects of psychological resources utilizing online survey data from 2,136 domestic and international students across five countries. The necessary condition analysis demonstrates psychological resources as necessary but not sufficient conditions for mental health and study adjustment. Additionally, one-way analysis of variance reveals that international students surpass domestic students in psychological resources, mental health, and adjustment. This research makes a novel contribution to the COR theory by proposing the “necessary resource principle,” which underscores that certain resources constitute necessary conditions in the event of significant losses of other resources. It provides evidence that individual psychological resources are not only desirable but also indispensable for students’ mental health and study outcomes during stressful periods. Furthermore, it contributes to adjustment theory by emphasizing the pivotal role of mental health in students’ adjustment. The implications for management and higher education 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 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.442
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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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