The Effectiveness of Terri Hiltel’s Self-Monitoring Program on Improving the Attention of the Students with Attention Deficit/Hyperactivity Disorder (ADHD)
Bibliographic record
Abstract
Introduction: This study aimed to examine Terri Hiltel’s Self-monitoring Program on improving the attention of four primary school students with Attention Deficit/Hyperactivity Disorder (ADHD). Materials and Methods: Single subject multiple-baseline design (ABA) across participants was utilized. The participants were observed along the baseline phase and the percentage of their target behaviors was accurately recorded. After the baseline, the students were trained by Terri Hiltel’s Self-monitoring program (called shiny light bulb method) for 12 sessions. Results: Level and trend analysis showed that the data points were placed at a level lower than the baseline for all the participants at intervention phase. That is, manifestation of off-task behaviors of the students in this phase decreased compared to the non-intervention phase (baseline); However, therapeutic effects discontinued and reduced at follow-up phase. Conclusion: Findings supported the effectiveness of self-monitoring program as a therapeutic cognitive-behavioral technique. The Study implications are discussed for applying this technique in schools and using it along with other treatments.
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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 | 0.000 | 0.001 |
| 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".