Study of seasonal variation in mood and behaviour in Northwestern Ontario / by Richard Alarie.
Bibliographic record
Abstract
A sample of 237 participants from the general and \nLakehead University student population were tested in \nJanuary 1992 using the Seasonal Pattern Assessment \nQuestionnaire, the Beck Depression Inventory, two \nsubscales (Demoralization and Mania) from the \nPsychiatric Epidemiology Research Interview and the \nFood/Drink Freguency Questionnaire. Climate, age and \noccupation seem to have an influence on the prevalence \nof seasonality in this sample. Three seasonality \ngroups (No-SAD, subsyndromal-SAD and SAD) were compared \nin terms of mood and food intake. Evidence that the \ndepressive symptoms of Seasonal Affective Disorder are \na by-product of core seasonality dimensions (increased \nappetite, fatigue and decreased energy) was found. A \nsubsample of individuals was tested monthly over a 12 \nmonth period to record changes in mood, behaviour and \nfood intake (using the same scales as in the initial \ntesting along with the NEO personality inventory). \nOverall, these yearlong participants reported seasonal \nchanges in mood? feeling worse in the fall and winter. \nThis pattern was more pronounced among the SAD group \nparticipants. The Food/Drink Frequency Questionnaire \ndid not provide clear results over the year.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".