Challenges of using progress monitoring measures: Insights from practicing clinicians.
Bibliographic record
Abstract
Although integrating progress monitoring (PM) measures into psychotherapy practice can provide numerous benefits, including improved client outcomes, relatively few clinicians use these measures (e.g., Ionita & Fitzpatrick, 2014). To better understand the reasons for clinicians' reluctance, consensual qualitative research methodology was used to examine the challenges faced by clinicians currently using PM measures. Open-ended, semistructured interviews, with 25 clinicians who chose to use PM measures, revealed that clinicians tended to face challenges involving technical concerns, negative responses from others, and personal barriers such as anxiety. The majority of participants discussed ways to overcome the challenges they experienced, including ensuring the fit of the PM measure, explaining measures to others to help engender a positive response, adapting their own perspective, and increasing their own and others' knowledge of the measures. Implications for practicing psychologists and for knowledge translation efforts are discussed.
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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) | 0.001 | 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 | 0.001 |
| 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".