Meeting People “Where They Are”
Bibliographic record
Abstract
PURPOSE: The propose of this discussion is to explore the role of the case manager to empower and motivate clients, especially those who appear "stuck" or resistant to change. Drawing upon the experiences of case managers across many different practice settings, the article addresses how case managers can tap into the individual's underlying and sometimes deep-seated desires in order to foster buy-in for making even small steps toward achieving their health goals. The article also addresses how motivational interviewing can be an effective tool used by case managers to uncover blocks and barriers that prevent clients from making changes in their health or lifestyle habits. PRIMARY PRACTICE SETTINGS: This discussion applies to case management practices and work settings across the full continuum of health care. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: The implication for case managers is deeper understanding of the importance of motivation to help clients make positive steps toward achieving their health goals. This understanding is especially important in advocating for clients who appear to be unmotivated or ambivalent, but who are actually "stuck" in engrained behaviors and habits because of a variety of factors, including past failures. Without judgment and by establishing rapport, case managers can tap into clients' desires, to help them make incremental progress toward their health goals.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".