A randomized, controlled trial of child psychiatric assessments conducted using videoconferencing
Bibliographic record
Abstract
We used a PC-based videoconferencing system to conduct child psychiatry assessments. The telecommunications link was six digital lines, giving a total bandwidth of 336 kbit/s. Twenty-three patients (aged 4-16 years), accompanied by their parents, completed two psychiatric assessments, one via videoconferencing and another face to face (FTF). The order of assessments was randomized. Questionnaires were used to record the diagnosis, treatment recommendations and the psychiatrists', patients' and their parents' satisfaction with each assessment. An independent evaluator concluded that in 22 cases (96%) the diagnosis and treatment recommendations made via the videoconferencing system were the same as those made FTF. The psychiatrists stated that videoconferencing assessments were an adequate alternative to FTF assessments and did not interfere with diagnosis. However, the responses from the psychiatrist satisfaction questionnaire showed that they preferred FTF assessments. No significant difference was found in the patients' or parents' satisfaction responses after the two types of assessment. The majority of children (82%) 'liked' using the telepsychiatry system and six (26%) preferred it to a FTF assessment. Most parents (91%) indicated that they would prefer to use the videoconferencing system than to travel a long distance to see a psychiatrist in person.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".