‘When the bedbugs come, that’s another problem’: exploring the lived experiences of bedbug infestations among low-income older adults and service providers who support them
Bibliographic record
Abstract
AIMS: Older adults in low-income housing communities are more vulnerable to bedbug infestations. Prior research, however, has predominately focused on the effectiveness of integrated pest-management strategies, with little attention given to the lived experiences of tenants struggling with infestations. We used a qualitative approach to explore what it is like to live with and treat bedbug infestations from the perspectives of low-income older adults and service providers. METHODS: = 58) who offer supports directly in the buildings. Semi-structured qualitative interviews and focus groups were used to explore the challenges of preparing and treating units for bedbugs, and examine how bedbugs impact access to support services. RESULTS: Bedbugs were a widespread issue, and underlying physical, mental, social, and financial challenges made it difficult for older tenants to prepare their units and access treatment. Tenants also faced bedbug stigma from community services, as many were unwilling to provide services in infested units. Although some service providers utilized strategies to minimize exposure, many were concerned these strategies created additional stigma. CONCLUSION: Our findings highlight an urgent need to increase public health funding to support older adults with the costs of bedbug elimination and to enhance pest-management strategies through partnerships with health and social service agencies to improve outcomes for older adults.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".