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Record W4205393657 · doi:10.2196/35776

A Brief, Daily, Online Mental Health and Well-being Intervention for University Staff During the COVID-19 Pandemic: Program Description and Outcomes Using a Mixed Methods Design

2022· article· en· W4205393657 on OpenAlexvenueno aff
Alexandra Parker, Sarah Dash, Matthew Bourke, Rhiannon K. Patten, Melinda Craike, Peter Baldwin, Warwick Hosking, Itamar Levinger, Vasso Apostolopoulos, Maximilian de Courten, Jenny Sharples, Monika Naslund, Vasileios Stavropoulos, Mary N. Woessner, Christopher C. Sonn, Caroline Stansen, Michaela C. Pascoe

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersVictoria University
KeywordsMental healthPsychological interventionPandemicAnxietyMedicineIntervention (counseling)PsychologyNursingCoronavirus disease 2019 (COVID-19)Medical educationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The unprecedented changes and isolation measures to contain COVID-19 have had multiple psychological and social impacts, with implications for professional and personal functioning. Evidence-informed interventions that can be rapidly implemented under pandemic conditions to support mental health during such times are urgently needed. OBJECTIVE: The aim of this study was to determine the acceptability and preliminary outcomes of a daily online mental health promotion program for tertiary education staff during the COVID-19 pandemic. METHODS: The "Victoria University (VU) Elevenses" program was delivered as an uncontrolled intervention at Victoria University (VU) in the western metropolitan region of Melbourne, Australia. In April 2020, an email invitation was sent to all academic and professional staff inviting them to: (1) participate in the program and (2) opt-in to the research component. The "VU Elevenses" program provided 10-15-minute microinterventions comprising lifestyle and well-being strategies to promote mental health via an online meeting platform at 11 AM each weekday. A mixed methods approach was used to evaluate the program, combining structured questionnaires with semistructured interviews to investigate the experiences of staff who participated in the program. RESULTS: Between 16 and 90 participants provided weekly program feedback. A total of 106 university staff opted into the longitudinal research component and 10 staff participated in the interviews. Participants reported high levels of satisfaction with sessions and perceived benefits for mental health. Approximately one quarter of participants reported moderate to severe symptoms of depression, anxiety, and stress at baseline, with significant reductions in these symptoms in the first 7 weeks of the program, corresponding with easing in mandatory isolation ("lockdown") restrictions. Symptoms of depression, anxiety, and stress all increased when lockdown measures were reintroduced, but not to the same levels as found during the initial lockdown period. Overall changes in depression and anxiety from baseline to the end of the program were explained by changes in COVID-19-related distress, whereas changes in self-compassion explained changes in stress. CONCLUSIONS: We show that it is feasible and acceptable to develop and deliver a program of brief interventions in a timely manner, using a simple and accessible online platform. Although participation in the program was initially associated with reduced symptoms of depression, anxiety, and stress, participants' mental health worsened with the reintroduction of a "lockdown" period. However, as symptoms of depression, anxiety, and stress did not return to levels observed at the start of the VU Elevenses program, participation in the uncontrolled intervention may have offered a protective benefit against the impact of the second significant lockdown period.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.296
GPT teacher head0.576
Teacher spread0.280 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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