Bibliographic record
Abstract
This study was conducted to explore the research question: Can students differentiate between healthy and unhealthy stress? How do coping mechanisms differ as number of different upcoming deadlines increase? It was predicted that students who are better able to differentiate between healthy and unhealthy stress will have healthier coping mechanisms, despite the number of upcoming deadlines that they have. Healthy coping mechanisms was defined as having a higher COPE inventory score on engagement compared to disengagement. Similarly, unhealthy coping was defined as a higher score for disengagement than engagement on the COPE inventory. Tobin (1985) defined engagement as the attempts by the individual to actively engage in efforts to manage their stressful situation. In contrast, disengagement was defined as strategies that likely result in the individual avoiding thoughts about the situation and refraining from behaviours that may change their stressful situation. One hundred students at The University of British Columbia were selected using a convenience-sampling method to take part in an online modified COPE inventory questionnaire. The results did not support our hypothesis. It was found that 59.4% of participants correctly identified their method of coping with stress (healthy or unhealthy), but although students were able to differentiate between healthy and unhealthy stress, this did not appear to be correlated with having healthier coping mechanisms. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".