Progress Monitoring Measures: The Interaction of Clinician Initial Motivation with Selection and Maintenance Issues
Bibliographic record
Abstract
The use of Progress Monitoring (PM) measures has been shown to improve outcomes in therapy for clients who do not follow the normal trajectory of improvement. In addition to improved outcomes, there are several other documented benefits of PM that may motivate clinicians to use PM. Research has examined the broader field of selecting mental health care quality assessment tools and a review of the literature has pointed to the importance of considering motivation for assessment when selecting a measure. However, how motivation influences the selection or maintained usage of PM measures has not been studied. This study examined initial motivation as well as measure selection and continuing use of PM. Consensual Qualitative Research methodology was applied to characterize how clinicians (n = 25) started, selected, and maintained use of PM measures and how initial motivation related to measure selection and continued use. Regardless of initial motivation, convenience and effectiveness emerged as important when selecting and continuing to use a measure. Results are compared to current frameworks for selecting mental health- care quality indicators. Our results suggest that PM measures need to strike a balance, emphasizing convenience as well as efficacy in order to improve clinical uptake and adherence.
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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 | 0.115 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".