Meaningful Activity and Boredom in the Transition from Homelessness: Two Narratives
Bibliographic record
Abstract
Background. Few studies have examined boredom and meaningful activity during the transition from homeless to housed, and those that exist are retrospective. Purpose. To prospectively examine how meaningful activities and boredom are experienced during the process of leaving homelessness. Method. Using a mixed-methods case study design, we interviewed 13 homeless participants at baseline using a 92-item quantitative interview, followed by a semi-structured qualitative interview. Two participants were located six months later and were interviewed again using the same protocol. Quantitative data are presented descriptively. Qualitative data were analyzed using narrative analysis. Findings. Qualitative data revealed two unique narratives of boredom and meaningful activity engagement in the transition from homeless to housed, with opportunities for engagement in meaningful activity limited largely by the social and housing environments in which both participants were situated. Quantitative data indicates that boredom and meaningful activity changed little before and after homelessness. At both baseline and follow-up, boredom scores for both participants were comparable to a sample of participants who were exposed to a “boredom” condition in an experimental study ( Hunter, Dyer, Cribbie, & Eastwood, 2016 ). Implications. Formerly homeless persons may struggle to engage in meaningful activity, and boredom may negatively affect mental well-being. Research with larger samples is needed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".