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Record W7008969300

Development and Psychometric Evaluation of the Post-Secondary Student Stressors Index

2019· dissertation· en· W7008969300 on OpenAlexafffundabout

Bibliographic record

VenueQSpace (Queen's University Library) · 2019
Typedissertation
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
FundersQueen's University
KeywordsStressorDistressTest (biology)Data collectionPersonalityMental stress
DOInot available

Abstract

fetched live from OpenAlex

Background: Over the past several years, reports of excessive stress and symptoms of languishing mental health have been increasingly reported among samples of Canadian post-secondary students. Chronic stress is highly correlated with negative mental health outcomes and formal diagnoses for common mental illnesses, such as depression and anxiety, have continued to climb. The purpose of this doctoral research program was to develop a new instrument to better assess the sources of post-secondary student stress. Existing instruments in this area are imperfect for a number of reasons, including weak (or lack of) psychometric analyses, poorly focused scope, and outdatedness. Importantly, few existing tools have involved students in the process of development. Methods: The development of the Post-Secondary Student Stressors Index (PSSI) spanned two years, and involved students as collaborators and subject matter experts over the course of the project. The instrument was designed to identify stressors specific to the post-secondary setting, with the aim of providing post-secondary institutions with a tool to identify the most significant sources of stress for students on their campuses. To facilitate item pool development, students participated in online surveys and focus group discussions. The initial item pool, derived from these qualitative responses, was then refined through the use of individual cognitive interviews and an online Delphi method. Finally, an online pilot test was conducted to assess psychometric properties. Results: The PSSI is composed of 46 stressors across five domains: academics, learning environment, campus culture, interpersonal, and personal. Students were asked to rate each stressor by severity and frequency. The tool demonstrated strong psychometric properties, with four types of validation evidence collected to support its validity: content, response processes, internal construct (including test-retest reliability), and relations to other variables. Conclusion: Our exploratory sequential mixed methods research design allowed for the development of a heavily context-based tool demonstrating strong psychometric properties. The PSSI can provide Canadian post-secondary institution administrators, student wellness staff, and program developers with a valid method of identifying the sources of student stress on their campus, and facilitate better targeting of mental health promotion and mental illness prevention efforts to best support students’ needs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.342
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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
Published2019
Admission routes3
Has abstractyes

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