Bibliographic record
Abstract
This article reviews some of the historical factors associated with the unprecedented strength and popularity of cognitive therapy, and offers predictions for the next half-century of this approach to treatment. It is predicted that the future will bring with it increased demands on cognitive therapy for evaluation of processes of change (including identification of therapeutic specifics and nonspecifics, technical specification of the process of therapy, and examination of therapist and patient predictors of change), and accountability and efficiency in the public and private sectors. With the increase in personal autonomy, globalization, and technology, the demands from the general public also will increase. One possible risk of the trend towards increased technology is that cognitive therapy may become overly technical. Although specific therapy techniques are crucial to delivering effective treatment, it is also the "nonspecifics" of therapy that add to the "art" of psychotherapy. The final challenge of cognitive therapy also may be the most difficult--to continue to be an empirically based science while maintaining its role in the art of healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".