Spiritual Formation: Challenges and Coping Mechanisms of Senior High Seminarians in the New Normal Education
Bibliographic record
Abstract
The education sector is one of the highly affected by the COVID- 19 pandemic. In the Philippines, various measures have been initiated to implement social isolation strategies, and online teaching is followed with rapid curriculum transformation. This study was conducted at a Seminary School in the Philippines to assess the spiritual formation activities, challenges and coping mechanisms based on the lived experiences of Senior High Seminarians. The study is a qualitative design using descriptive phenomenology as methodology. Data were gathered using a researcher-made interview schedule and questionnaire for the focus group discussion (FGD). Ten senior High School seminarians were chosen through purposive sampling. The result shows that most seminarians attended the Mass and praying novena and holy rosary as part of their spiritual formation activities. However, they spent their time playing online mobile games and are addicted to social media, leading them to lack focus and motivation. Seminarians resort to prayer, self-disciplining through avoidance or limitation in using gadgets and social media, and effective and proper to cope with these challenges. It is recommended that the seminary conduct capacity building on how to combat challenges seminarians face and provide a venue for open discussion and feedback so they may have the chance to voice their sentiments and concerns.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".