A Qualitative Exploration of the Information and Service Needs of Youth Siblings of Individuals with Cystic Fibrosis
Bibliographic record
Abstract
Cystic Fibrosis (CF) is a progressive, fatal inherited disease common amongst Caucasian children and young adults in Canada. In addition to being one of the most difficult chronic health conditions to manage, siblings of children and adolescents with CF have been shown to experience elevated psychological symptoms (e.g., depression and anxiety), negative social adjustment, and internalizing behaviours. While the need to address these concerns by providing appropriate information and services is evident, to date there are no tailored mental health programs designed for siblings of children and adolescents with CF. There were five main objectives for the present research: To explore (1) what challenges healthy siblings of youth with CF face, (2) what information about CF healthy siblings of youth with CF need, (3) what types of mental health services healthy siblings of youth with CF need, (4) the perceived benefits and/or drawbacks of obtaining services via the Internet, and (5) what components should be included in an interactive, Internet-delivered mental health prevention program for healthy siblings of youth with CF. Participants (n = 10) included four youth siblings of pa Mage = 36.67, SD = rent caregivers ( a six nd w Mage = 10.25, SD = 2.77) ith CF ( yout h 3.40) recruited from CF clinics and chapters in Saskatchewan, Canada. Siblings and parent caregivers completed brief demographic questionnaires and participated in individual semi-structured interviews. Thematic content analysis was used to analyze the data collected from each participant group with reference to the main study objectives. Sibling participant responses uncovered four major themes: (1) life balance, (2) emotional challenges, (3) coping, and (4) bridging gaps in sibling care. Parent responses uncovered six major themes: (1) life balance, (2) emotional challenges, (3) coping, (4) developing and maintaining relationships, (5) education, and (6) improving mental health services. The results of the present study highlight the unique challenges experienced by siblings and families of youth with CF and the need for effective family support in coping with CF. Information obtained from the study will be used to inform the development of an evidence-based, Internet-delivered mental health program aimed at improving the overall mental health functioning and quality of life of this population.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".