Mood Change of English, French and Chinese Immigrants in Ottawa-Gatineau Region, Canada
Bibliographic record
Abstract
This multicultural study aimed at examining moodchange of English, French and Chinese speaking immigrants in Ottawa-Gatineau Region, Canada, and identifying demographic factors that impact the change. 810 immigrants of English, French and Chinese speaking sub-groupswere recruited by purposive-sampling. Using self-reports, respondents answered questions regarding moodchange (moodstatus change and mood belief change) and demography in Multicultural Lifestyle Change Questionnaire of English, French or Chinese version. Data were analyzed statistically for the different immigrant sub-groups. Immigrants of different gender, language and category sub-groups exhibited different Mood Change Rates, Mood Improving Rates,Mood Declining Rates and MoodBelief Change Rates. There was no statistical difference between the ratesof immigrant sub-groups.Mood Change (MoodStatus Change + MoodBelief Change) was correlated positively with Mother Tongue and negatively with Speaking Languages. Mood Status Change was negatively correlated with Marital Status and Highest Level of Education. Mother Tongue, Speaking Languages and Highest Level of Education significantly impacted MoodChange (Mood Status Change + Mood Belief Change). Marital Status and Highest Level of Education significantly influenced Mood Status Change. Immigrants of different sub-groups in Canada experienceddifferentmoodchanges. Marital Status and Highest Level of Educationwerethe main factors impacting Mood Status Change. Mother Tongue and Speaking Languages werethe principal factors influencing Mood Belief Change. Culture was an important factor contributing Mood Change. Acculturation could impact Mood Status Change and Mood Belief Change. Data of immigrant mood change can provide evidence for health policy-making and policy-revising in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".