Siblings of Those With and Without Mental Illness: Differences in Life Decisions and Depressive Symptoms
Bibliographic record
Abstract
Young adulthood is a difficult period -individuals begin to separate from their families and transition into adulthood, focusing more on themselves and the future.This study aimed to better understand psychosocial and individual differences that contribute to different well-beings for young adults that have siblings without (n=156) and with (target sibling) (n=121) mental illness.Target siblings reported more depressive symptoms and less positive influence of siblings on life decisions.Closeness was related to well-being, but did not interact with sibling type to influence well-being.Both sibling types reported little support and unsupport, and did not differ in coping.Target siblings reported little sibling influence on life decisions at lower levels of perfectionism, but a strong impact at higher levels.This study: confirmed previous findings that sibling types differ in depression; extended findings to include differences in life decisions; and identified factors that did and did not influence well-being.the pleasure of seeing most all my reactions to working through my master's (from brainfried to having the seemingly uncontrollable giggles!) and knew when to take me away from the computer to recharge.I would like to give a very special thanks to my parents and my brother for "allowing" me to post an online story about being a sibling of someone with mental illness in order to attract this very difficult-to-reach population.After all, although it was from my perspective, it meant exposing parts of all of you as well, and I know that it was not easy to allow yourselves to be vulnerable in that way.And for that I am so very grateful because it made it that much easier to expose myself and to be invested in my research.Lastly, but certainly not forgotten, immense thanks to my supervisors, Drs.Kim Matheson and Hymie Anisman, for suggesting I take on studying those who, like myself, have a sibling with mental illness.Your understanding and unwavering support, kindness, and patience as I worked my way through, not only writing my thesis, but also my own emotions regarding being a sibling of someone with a mental illness has helped me academically and personally.My extended stay as aCarleton student has led some friends to believe that I would rather be a student forever (and perhaps yourselves too!).Despite the delays, the life advice you have given and your determination to help me graduate have opened my eyes to a future with more possibilities than I could have ever imagined for myself.I will be forever grateful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".