Humanistic psychotherapy research 1990–2015: From methodological innovation to evidence-supported treatment outcomes and beyond
Bibliographic record
Abstract
OBJECTIVE: Over the past 25 years, humanistic psychotherapy (HP) researchers have actively contributed to the development and implementation of innovative practice-informed research measures and coding systems. METHOD: Qualitative and quantitative research findings, including meta-analyses, support the identification of HP approaches as evidence-based treatments for a variety of psychological conditions. RESULTS: Implications for future psychotherapy research, training, and practice are discussed in terms of addressing the persistent disjunction between significant HP research productivity and relatively low support for HP approaches in university-based clinical training programs, funding agencies, and government-supported clinical guidelines. CONCLUSION: Finally, specific recommendations are provided to further enhance and expand the impact of HP research for clinical training programs and the development of treatment guidelines.
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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 | 0.213 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".