Identifying Major Depressive Symptoms and Major Depressive Episodes in Adolescents with Cancer
Bibliographic record
Abstract
Cancer is the leading cause of death from disease among adolescents in Canada. Although cancer is a well-researched disease process in the medical and nursing literature, adolescence is relatively understudied, with minimal evidence on strategies to promote healthy emotional adjustment throughout the disease trajectory. Current literature on major depressive episodes (MDE) in adolescents diagnosed with cancer suggests limited evidence in screening and diagnosing MDEs. Furthermore, current practice in pediatric oncology centres do not include routine psychosocial assessments of adolescents with cancer, but rather, rely on individual clinician discretion on individuals who may benefit from referrals to a psychosocial and/or psychiatric team. Existing instruments used to screen for depression in adolescents have not previously been tested for clinical utility in pediatric oncology patients, which is problematic due to the significant overlap in MDE symptoms and adverse side effects of cancer disease and treatment. Research on adult cancer survivors suggests that age, gender, and anxiety are significantly related to depression, but this has not previously been examined among Canadian adolescent cancer patients. This study employed a cross-sectional descriptive design to examine and compare the feasibility of utilizing the Children’s Depression Inventory (CDI) and the Diagnostic Interview for Children and Adolescents (DICA-IV) to screen for MDEs, and to examine the relationships among age, gender, and anxiety and a MDE in adolescents with cancer. Of the twenty five eligible participants, fourteen adolescent patients with either a malignant cancer or tumour requiring chemotherapy treatment or a hematological disorder requiring a blood or bone marrow transplant were recruited from an outpatient pediatric oncology clinic. The CDI was found to be a feasible tool that can be used in busy clinical settings, as it was less time-intensive compared to the DICA-IV. Further comparison of the CDI and DICA-IV indicated that there was no evidence that participants were more willing to disclose their symptoms on a self-report questionnaire compared to a face-to-face interview. As for recruitment issues, females were more willing to participate in the study than males, but overall, adolescents as a group were a difficult population to engage in the study, with only a 56% participation rate in this study. Future research will need to address these recruitment challenges. Finally gender (p=0.013) and anxiety (p=0.003) were significantly correlated with a MDE.
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 | 0.001 | 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) | 0.000 | 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 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".