EXPERIENCES AND WELL-BEING OF LOW-INCOME WORKERS IN THE SENIOR COMMUNITY SERVICE EMPLOYMENT PROGRAM
Bibliographic record
Abstract
Abstract The Senior Community Service Employment Program (SCSEP) is the only federal job-training program specifically for older workers. SCSEP enrolls unemployed older workers with incomes at or below 125% of the federal poverty level into on-the-job and classroom-based training. SCSEP participants often struggle to secure unsubsidized work, as they experience, on average, more than 3 barriers to employment (e.g., low literacy levels, disability, limited English proficiency). While some outcome metrics are tracked (e.g., job attainment, median unsubsidized wages), we know relatively little about participants’ health, well-being, and training experiences. In response, and in partnership with the Massachusetts Executive Office of Elder Affairs, we fielded a survey on the multidimensional health, well-being, and experiences of participants throughout Massachusetts. Fielded in the spring and summer of 2022 and in six languages, a total of 91 SCSEP participants took the survey with an age range of 57 to 82. Almost half (44%) spoke a language other than English at home, of which Cantonese and Vietnamese were the most common. Respondents generally reported being in moderately good health (only 3% reported “excellent” health) with at least one chronic health condition (86%), and two-thirds (66%) reported reduced social activities due to the COVID-19 pandemic. Less than one-quarter (22%) reported having money left over at the end of the month, but that SCSEP itself improved their finances, social engagement, and self-confidence. To conclude, we will offer reflections on the importance of tracking additional characteristics of SCSEP participants and engaging in respectful community-based work with diverse older populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".