Exploring Sleep Behaviors and Routines, and Mindfulness Practices Among Seniors Residing in Low-Income Housing
Bibliographic record
Abstract
Abstract Poor sleep is common among older adults and the general population. Low socioeconomic status is associated with sleep disturbance and short sleep duration. Sleep disturbance is associated with an increased risk for Alzheimer’s disease and incident dementia. Evidence indicates that mindfulness can improve sleep duration and quality. We conducted a qualitative study of sleep and mindfulness practices among seniors in a low-income housing facility in the US Northeast. Participants completed the Montreal Cognitive Assessment (MoCA), a brief demographic questionnaire, and an interview about sleep and mindfulness. Interviews were audio-recorded and transcribed verbatim. Qualitative analysis proceeded in accordance with the Constant Comparative Method. Among participants (n = 12), average age was 70.1 (s.d.=6.7); 83.3% of participants were White and 16.7% were Black. Participants were 50% male. Half the sample reported sleeping 7 hours, 25.0% 6 hours, and 25% 8 or more hours. Average score on the MoCA was 23.3 (s.d.=4.03). Common issues related to healthy sleep included taking long naps, nighttime television use, waking from sleep and having trouble falling back asleep, and nighttime checks interrupting sleep conducted by staff members of the residents. Participants reported limited awareness of mindfulness, but in one case, that learning mindfulness skills was ‘life changing.’ Several reported attempting mindfulness, but being unable to implement a regular practice. Another resident mentioned interest in social support for mindfulness practice. Results from this study highlight sleep struggles, and opportunities for tailoring sleep and mindfulness content in programs for older adults residing in low-income housing.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".