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Record W2980794745 · doi:10.1017/jrr.2019.19

University Blues: Role of Attachment and Distress on Students’ Evaluations of Instructors’ Teaching Performance

2019· article· en· W2980794745 on OpenAlexaff
Jessica Reid, Elaine Scharfe

Bibliographic record

VenueJournal of Relationships Research · 2019
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsTrent University
Fundersnot available
KeywordsAnxietyPsychologyDistressAssociation (psychology)PerceptionClinical psychologyDevelopmental psychologyMedical educationPsychotherapistMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Despite concerns about bias, student evaluations of teaching continue to be significant to faculty career advancement in academia. In a recent study, attachment representations were shown to be associated with students’ perceptions of instructors (Henson & Scharfe, 2011); students with insecure-anxious representations were more likely to rate their professors negatively. These data, however, were cross-sectional, and the role of distress in this association was not examined. To examine the influence of anxiety and depressive symptoms on the association between attachment representations and evaluations of instructors’ teaching performance, 102 undergraduate students (91% female, 17–38 years old) completed questionnaires at two time points during the semester. Interestingly, both attachment anxiety and avoidance measured at the beginning of the semester were negatively associated with teaching evaluations at the end of the semester, and this effect was stronger for participants who reported high anxiety and depressive symptoms. The findings are consistent with previous work exploring the perception of others of depressed and non-depressed individuals, and provides some support for Bowlby's original proposals concerning the importance of distress in understanding the effects of attachment. Strategies to support students’ transition to post-secondary education and to promote positive teaching evaluations 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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.196
GPT teacher head0.525
Teacher spread0.330 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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