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Record W7071405764

The Theory of Planned Behaviour Approach to Identifying Predictors of Intentions to Seek Help for Mental Health Issues Among Post-Secondary Students Amidst the COVID-19 Pandemic

2023· dissertation· en· W7071405764 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of planned behaviorMental healthNormativePsychological interventionSocial norms approachFormative assessmentControl (management)Salient
DOInot available

Abstract

fetched live from OpenAlex

Mental health challenges among Canadian post-secondary students have been on a steep upward trend in recent years, exacerbated by the COVID-19 pandemic. Yet, many students do not reach out to the mental health services available to them. To lessen or remove actual and perceived barriers, research has been exploring how to predict their intentions to seek help for their personal mental health challenges. Much of this work applies the widely used Theory of Planned Behaviour (TPB; Fishbein & Ajzen, 1980) which posits that behavioural intentions are best predicted by attitudes toward the behaviour; perceived normative expectations; and perceived behavioural control. Though the theory also posits that beliefs underlie and are formative of these three predictors, studies have largely neglected their measurement. This is problematic as it deviates from the TPB and frustrates efforts toward the development of interventions to enhance behaviours towards seeking help for mental health challenges. If they are to be effective, such interventions must be directed at changing salient beliefs. This research addresses this gap through a mixed-method sequential design. It provides a unique and valuable contribution to scholarship and practice by identifying and examining the role of students’ attitudinal, normative, and control beliefs with respect to their intentions to seek help. This is examined separately and together with the more traditionally studied direct predictors of attitudes, subjective norms, and perceived behavioural control (i.e., the formative constructs defined by the beliefs). Students relied on six salient beliefs concerning their intentions to seek mental health help: two behavioural, two normative, and two control beliefs. Student attitudes, subjective norms, and PBC mediated the links between salient beliefs and intentions, with PBC being the strongest predictor of intentions to help-seek. The findings help inform interventions to change the beliefs most associated with low intentions to get mental health support.

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)0.014
metaresearch head score (Gemma)0.023
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.295
Teacher spread0.258 · 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
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
Published2023
Admission routes1
Has abstractyes

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