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Record W4386369504 · doi:10.1007/s11618-023-01185-5

Adjusting to college—Do ability beliefs and confidence in getting support matter for performance and mental health?

2023· article· en· W4386369504 on OpenAlexaboutno aff
Luise von Keyserlingk, Julia Moeller, Jutta Heckhausen, Jacquelynne S. Eccles, Richard Arum

Bibliographic record

VenueZeitschrift für Erziehungswissenschaft · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersEberhard Karls Universität Tübingen
KeywordsQuarter (Canadian coin)PsychologyPandemicMental healthSocial supportPsychological distressMedical educationCoronavirus disease 2019 (COVID-19)Social psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Entering college, students are required to adjust to a new academic and social environment. During the COVID-19 pandemic, social interactions with peers and faculty were limited to online settings and access to campus resources was restricted. Hence, students who entered college in fall 2020 began their freshman year under particularly challenging circumstances. We used data from two freshman cohorts, who started college either before or during the pandemic. We investigated to what extent mid-quarter academic and social adjustment (i.e., ability beliefs and confidence in getting support) predicted end-of-quarter performance, psychological distress, and satisfaction of freshman students. Results showed that students who started college during the pandemic were less confident they could get support by peers in the middle of their first quarter. Furthermore, students from the second cohort reported higher psychological distress and lower satisfaction with their adjustment at the end of their first quarter. Results showed that ability beliefs played an important role for end-of-quarter performance, whereas confidence in getting support was more relevant for psychological well-being outcomes in both cohorts.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.400
Teacher spread0.366 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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