Consensus statement on the problem of terminology in psychological interventions using the internet or digital components
Bibliographic record
Abstract
Since the emergence of psychological interventions delivered via the Internet they have differed in numerous ways. The wealth of formats, methods, and technological solutions has led to increased availability and cost-effectiveness of clinical care, however, it has simultaneously generated a multitude of terms. With this paper, we first aim to establish whether a terminology issue exists in the field of Internet-delivered psychological interventions. If so, we aim to determine its implications for research, education, and practice. Furthermore, we intend to discuss solutions to mitigate the problem; in particular, we propose the concept of a common glossary. We invited 23 experts in the field of Internet-delivered interventions to respond to four questions, and employed the Delphi method to facilitate a discussion. We found that experts overwhelmingly agreed that there were terminological challenges, and that it had significant consequences for conducting research, treating patients, educating students, and informing the general public about Internet-delivered interventions. A cautious agreement has been reached that formulating a common glossary would be beneficial for the field to address the terminology issue. We end with recommendations for the possible formats of the glossary and means to disseminate it in a way that maximizes the probability of broad acceptance for a variety of stakeholders.
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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 | 0.337 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".