Utilizing Digital Media for Guidance and Counseling in Education
Bibliographic record
Abstract
This causal correlational study aims to find out the effect of factors of digital technology on guidance and counseling teachers’ usage of guidance and counseling media. It involved eighty-five guidance and counseling teachers in seven regions, recruited using a convenience sampling technique. Data were garnered using the Attitude scale for digital technology developed by Emine [1], which has been considered valid and highly reliable (1.000). Data were analyzed using a linear regression test and path analysis using SPSS. The effect size of these factors varied, with the highest score observed in “technology for me” (75.1%), followed by interest in technology (70.3%), technological use (63.9%), competence (59.6%), social network (54.2%), conscious use (53.7%), recreational use (43.2%), and negative aspects (25.2%). These factors exhibited significant correlations with and influenced the usage of guidance and counseling media among guidance and counseling teachers in this study. Consequently, it is necessary to implement interventions aimed at enhancing the utilization of digital technology for guidance and counseling services.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".