Biases in research: risk factors for non-replicability in psychotherapy and pharmacotherapy research
Bibliographic record
Abstract
Replicability of findings is an essential prerequisite of research. For both basic and clinical research, however, low replicability of findings has recently been reported. Replicability may be affected by research biases not sufficiently controlled for by the existing research standards. Several biases such as researcher allegiance or selective reporting are well-known for affecting results. For psychotherapy and pharmacotherapy research, specific additional biases may affect outcome (e.g. therapist allegiance, therapist effects or impairments in treatment implementation). For meta-analyses further specific biases are relevant. In psychotherapy and pharmacotherapy research these biases have not yet been systematically discussed in the context of replicability. Using a list of 13 biases as a starting point, we discuss each bias's impact on replicability. We illustrate each bias by selective findings of recent research, showing that (1) several biases are not yet sufficiently controlled for by the presently applied research standards, (2) these biases have a pernicious effect on replicability of findings. For the sake of research credibility, it is critical to avoid these biases in future research. To control for biases and to improve replicability, we propose to systematically implement several measures in psychotherapy and pharmacotherapy research, such as adversarial collaboration (inviting academic rivals to collaborate), reviewing study design prior to knowing the results, triple-blind data analysis (including subjects, investigators and data managers/statisticians), data analysis by other research teams (crowdsourcing), and, last not least, updating reporting standards such as CONSORT or the Template for Intervention Description and Replication (TIDieR).
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 | 0.666 | 0.829 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".