Life course psychosocial precursors of parent mental health resilience during the COVID-19 pandemic: A three-decade prospective cohort study
Bibliographic record
Abstract
BACKGROUND: There has been widespread interest in the implications of COVID-19 containment measures on the mental health of parents. Most of this research has focused on risk. Much less is known about resilience; yet such studies are key to protecting populations during major crises. Here we map precursors of resilience using life course data spanning three decades. METHODS: The Australian Temperament Project commenced in 1983 and now follows three generations. Parents (N = 574, 59 % mothers) raising young children completed a COVID-19 specific module in the early (May-September 2020) and/or later (October-December, 2021) phases of the pandemic. Decades prior, parents had been assessed across a broad range of individual, relational and contextual risk and promotive factors during childhood (7-8 years to 11-12 years), adolescence (13-14 years to 17-18 years) and young adulthood (19-20 years to 27-28 years). Regressions examined the extent to which these factors predicted mental health resilience, operationalised as lower than expected anxiety and depressive symptoms during the pandemic relative to pre-pandemic symptoms. RESULTS: Parent mental health resilience during the COVID-19 pandemic was consistently predicted by several factors assessed decades before the pandemic. These included lower ratings of internalizing difficulties, less difficult temperament/personality traits and stressful life events, and higher ratings of relational health. LIMITATIONS: The study included 37-39-year-old Australian parents with children age between 1 and 10 years. DISCUSSION: Results identified psychosocial indicators across the early life course that, if replicated, could constitute targets for long-term investment to maximise mental health resilience during future pandemics and crises.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".