Bibliographic record
Abstract
Anxiety represents one of the most commonly diagnosed mental illnesses among adults in the United States, affecting an estimated 19.1% of the adult population annually, with a lifetime occurrence of 31.1% (NIMH, 2017). This retrospective study intended to assess whether qEEG guided amplitude neurofeedback (NF) is a viable treatment for anxiety symptom reduction. 40 participants were assessed for anxiety using symptom and EEG measures. Demographics include age ranges from 19-62 (M = 37.7, SD =13.87). Gender identification comprised 21 male and 19 female. 15 clients self-identified as White (Non-Latino) (38%), 14 as Latino/Latina (35%), and 11 did not self-report ethnicity (28%). Pre/post-assessments were given to the participants. Symptom assessments included the Zung Self-Rating Anxiety Scale and Achenbach (ASEBA) Adult Self Report (ASR). A qEEG was used to determine protocols for each participant. Participants were scheduled to receive 30-minute NF treatment sessions twice a week for one academic semester. The range of attended sessions was 7-19 (M = 12.72, SD = 2.78). Accurate number of session data was unavailable for 4 of the subjects. Symptom measures showed statistically significant improvement. Limitations include small sample size and no control group or sham NF group. Suggestions are included for future studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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; 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".