Opiskelijan hyvinvointi koetuksella? : korkeakouluopiskelijoiden kokemat terveys- ja toimintarajoitteet, digitaalinen osaaminen ja opiskelu pandemian aikana
Bibliographic record
Abstract
The number of health problems and disabilities among Finnish higher education students has increased throughout the 21st century. In this article, the Eurostudent VIII data is addressed, where almost half (46.6%) of the students reported one or more health problems and almost a quarter (23.1%) reported a mental health problem. The pandemic era changed everything related to studies, highlighting the role of digitalization. The results show that students rated digital tools as important both in their daily lives and in their studies. The majority of students felt that the pandemic had reduced the quality of teaching and motivation to study. For the most part, this was not felt to have affected the duration of studies or academic performance. However, students who reported mental health problems or learning difficulties were most likely to feel that the pandemic affected negatively to their study pace, and almost half of those who reported learning difficulties felt that the pandemic had had a negative impact on their academic performance. Multivariate analyses profiled the winners and losers of the pandemic era: those whose learning experience became negative, whose digital skills were insufficient and who missed face-to-face teaching; and those whose motivation even increased and who, being digitally active, preferred distance learning.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".