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Record W4200233206 · doi:10.46542/pe.2021.211.810816

Students’ proposed self-management strategies in response to written cases depicting situations of adversity

2021· article· en· W4200233206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePharmacy Education · 2021
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychologyConstruct (python library)Thematic analysisHarassmentEmotional exhaustionBurnoutPsychological resilienceSocial psychologyApplied psychologyQualitative researchClinical psychology

Abstract

fetched live from OpenAlex

Introduction: Pharmacy students are facing academic and non-academic pressures that require emotional regulation. This study explored students’ possible self-management strategies when encountering situations known to deplete resilience. Methods: This was a qualitative think-aloud study designed to elicit final year pharmacy students’ reactions to situations known to deplete resilience and evoke emotional responses (racism, lack of trust, negative feedback, burnout, personal stress, sexual harassment). Thematic analysis was used to capture the strategies students used to self-manage their emotions. Results: Students made use of three types of processes to self-manage their emotions, which were used to construct three overarching strategies: the internalizer (avoidance, self-reflection), the seeker (asking for help or corroboration), and the confronter (approaching the situation and persons involved ‘head on’). Conclusion: Findings support the notion that students’ self-management is not a ‘one size fits all’ construct, and any approach to emotional skill development needs to recognize individualization within student responses.

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.

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)0.000
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 score0.073
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.059
GPT teacher head0.445
Teacher spread0.386 · 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