“It’s like your days are empty and yet there’s life all around”: A mixed methods, multi-site study exploring boredom during and following homelessness
Bibliographic record
Abstract
PURPOSE: To identify experiences of boredom and associations with psychosocial well-being during and following homelessness. METHODS: Using a convergent, mixed-methods explanatory design, we conducted quantitative interviews with 164 participants) (n = 102 unhoused; n = 62 housed following homelessness) using a 92-item protocol involving demographic components and seven standardized measures of psychosocial well-being. A sub-sample (n = 32) was approached to participate in qualitative interviews. Data were analyzed by group (unhoused; housed). Quantitative data were analyzed using descriptive statistics designed to generate insights into boredom, meaningful activity engagement, and their associations with psychosocial well-being during and following homelessness. Qualitative data were analyzed using thematic analysis. Quantitative and qualitative findings were integrated at the stage of discussion. RESULTS: Quantitative analyses revealed small to moderate correlations between boredom and increased hopelessness (rs = .376, p < .01), increased drug use (rs = .194, p < .05), and lowered mental well-being (rs = -.366, p < .01). There were no statistically significant differences between unhoused and housed participants on any standardized measures. Hierarchical regression analyses revealed that housing status was not a significant predictor of boredom or meaningful activity engagement (p>.05). Qualitative interviews revealed profound boredom during and following homelessness imposing negative influences on mental well-being and driving substance use. CONCLUSIONS: Boredom and meaningful activity are important outcomes that require focused attention in services designed to support individuals during and following homelessness. Attention to this construct in future research, practice, and policy has the potential to support the well-being of individuals who experience homelessness, and to contribute to efforts aimed at homelessness prevention.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".