THE FINAL WORD: CT MFP PARTICIPANTS’ QUALITATIVE FEEDBACK ON PROGRAM EXPERIENCES
Bibliographic record
Abstract
The Money Follows the Person Rebalancing Demonstration (MFP) is a federal initiative to help states transition people needing long-term services and supports from institutional settings to the community in an effort to enhance individual preferences and rebalance Medicaid expenditures. This qualitative study explored Connecticut (CT) MFP consumers’ post-transition experiences. Participants provided feedback about perceived effectiveness of the program and identified barriers to successful program implementation. Data were collected from 1,197 consumers who answered an open-ended question in the Quality of Life Survey conducted from 2008–2015 at baseline, 6, 12 and 24 months after transition. ATLAS.ti software was used to identify and organize themes. Findings show that while consumers appreciated the program and offered positive feedback, many also reported barriers and challenges. Of the 1,902 comments, the most frequently reported theme was lack of support for physiological needs, such as additional personal assistance and assistive technology (40%, n=764). The second most common theme was shortages in supplemental supports, such as housing, transportation and finances (25%, n=484). A third theme referred to social needs including choice and control, and social supports (18%, n=311). While MFP consumers generally report high quality of life, both in the CT program and nationally, this study suggests that some participants would benefit from more community services and supports to fully meet their needs. Findings have the potential to deepen stakeholders’ understanding of consumer needs and facilitate planning to improve services and supports for older adults and people with disabilities living in the community.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".