Exploring Patient‐Related Contextual Factors and Personal Reflections About the Managing Cancer and Living Meaningfully (CALM) Intervention for Adults With Advanced Cancer in Metropolitan and Non‐Metropolitan Southern Alberta: A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The evidence-based Managing Cancer and Living Meaningfully (CALM) psychotherapeutic intervention was designed to address the complex needs of those with advanced cancer. Ample evidence supports the efficacy of CALM therapy; less is known about the patient-specific factors that influence initiation and continuation of CALM sessions. AIMS: To gain understanding of patient-specific factors and referral routes that influence initiation and continuation of CALM. METHODS: An Interpretive Description framework and concurrent triangulation mixed-methods design was used to analyse baseline patient-specific variables for prediction of engagement (number of sessions) in CALM following recruitment from cancer centres, palliative care services, and community cancer care organisations across Southern Alberta, Canada. Patient input (n = 10) occurred through semi-structured interviews exploring experiences with advanced cancer, CALM referral and engagement. RESULTS: Among consented individuals (n = 69), those directly referred by healthcare providers (HCPs) and self-referred (total n = 32), engaged in more CALM sessions (M = 4.97, SD = 3.51) than those referred indirectly (M = 3.19, SD = 2.26, p < 0.05), particularly younger participants (< 65 years) and those with longer life expectancy (> 10 months). Participants chose CALM based on experiences of distress, wanting to talk openly, and expecting benefit. CONCLUSIONS: Greater patient engagement in the CALM intervention following HCPs' direct referrals may be based on trust in the HCP-patient relationship, and accurately prognosticating sufficient physical well-being for participation and benefit. Future health systems research may evaluate systematic programing with offering CALM referrals following an advanced cancer diagnosis.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".