Exploring AgrAbility Quality of Life Profiles
Bibliographic record
Abstract
OBJECTIVES: AgrAbility provides information, education, and services to agricultural workers with disabilities. There is a dearth of knowledge about the variability in the quality of life (QoL) domains associated with AgrAbility program involvement. This study examined QoL profiles at two time points with individuals seeking services related to QoL domains including physical, psychological, social, and existential well-being. The profiles were described based on demographic variables to understand who may be in these profiles. METHODS: The sample consisted of 1,358 farmers and ranchers with disabilities who completed the McGill Quality of Life (MQOL) survey before receiving AgrAbility services (time one), and 343 of whom completed a follow-up QoL survey after receiving AgrAbility services (time two). Latent profile analysis was employed to examine groupings of individuals on the variables of physical, psychological, existential, and social well-being. Descriptive analysis of profile membership and predictive models were used to understand the profiles and their relationship across time. Analyses were performed using Mplus version 8.11. RESULTS: Three QoL profiles were identified. The Low QoL profile had the most females, while the High QoL profile had the least. There were no significant relationships identified between sex, work status, and age, and profile membership. The High QoL profile was marked by high scores on QoL indicators of psychological, social, and existential well-being. The Low QoL had almost an opposite pattern. At time 2 assessment, individuals tended to move to a higher QoL profile. In general, the probability of moving to a lower profile was below 0.10. CONCLUSIONS: Heterogeneity is present in QoL indicators among individuals who worked with their State AgrAbility Team to accomplish their goals. Profile movement supports the benefits of receiving AgrAbility services for increasing QoL. These profiles can be used to better understand the needs of individuals and the direct services to address those needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".