Association between fidelity to the strengths model of case management and client outcomes: A quasi-experimental study.
Bibliographic record
Abstract
OBJECTIVE: Evidence concerning strengths model of case management (SMCM) remains mixed. This study aimed to test the hypotheses that higher fidelity to SMCM is associated with improved quality of life (QoL), hope, community participation, community functioning, more days of competitive employment and of independent living, and fewer days of hospitalization. METHODS: = 311) answered standardized questionnaires at 4.5-month intervals up to 18 months. Fidelity assessments were interpolated so they could be associated with questionnaire responses. Linear mixed-effects models and generalized linear models were estimated. RESULTS: Fidelity increased significantly, with all sites except one achieving or approaching good fidelity within 36 months. Fidelity was not significantly associated with any of the outcome measures, although all estimated directions of relationships were consistent with our hypotheses. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: In a pragmatic study of real-world implementation of the strengths model at seven sites, no statistically significant relationships between fidelity and outcomes were found. Low variation in fidelity across individuals, modest sample size, and limited ability to detect change over 18 months, may have contributed to these null findings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".