The role of feeling understood in men's loneliness-depression pathway: Longitudinal findings over three assessment waves
Bibliographic record
Abstract
INTRODUCTION: Loneliness is a significant global public health issue associated with a range of negative health outcomes. Men experiencing loneliness are at increased risk for depression; however, limited research has focused on mechanisms that explain the relationship between men's loneliness and depression. Existential isolation, the lack of feeling deeply understood by others, may be an important element that provides insight into how loneliness is related to men's depression over time, as evidence suggests that masculine norms may impair men's abilities to seek out help and experience emotional intimacy. The purpose of this study was to examine existential isolation as mediator between men's loneliness and depression over time. We hypothesized that the relationship between men's baseline loneliness and depression six months later would be mediated by their sense of feeling understood at three months. MATERIALS AND METHODS: An international community sample of men (n = 300) were recruited from the men's mental health website HeadsUpGuys and completed anonymous online surveys at three time points, including baseline, three months, and six months. The longitudinal design involved self-reported assessment of participants' loneliness, sense of feeling deeply understood, and depressive symptoms at all timepoints. RESULTS: The mediation model revealed men's perception of feeling deeply understood by others to be a significant temporal mediator of the association between loneliness and depression. CONCLUSIONS: Interventions that reduce or prevent men's loneliness and existential isolation may significantly reduce men's depression risk.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| 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".