Resource utilization among informal caregiver of lung cancer patients undergoing treatment
Bibliographic record
Abstract
The objective of this study is to explore patient and caregiver factors that shape the use of available resources to support caregiving for lung cancer patients undergoing treatment. A mixed-method study was conducted at one regional cancer centre within the Province of Ontario, Canada, using concurrent triangulation design. Adult patients with lung cancer (n=46) and their caregivers (n=42) (37 patient-caregiver dyads) were invited to complete a one-time study survey. Informal caregivers (n=20) also participated in a one-time semi-structured interview. Descriptive statistics and Pearson’s correlation were used to examine patterns of resource utilization and associations among study variables. Content analysis was conducted to analyse data from interviews. Informal caregivers demonstrated low overall resource utilization. Education materials and homecare support were the most frequently used but perceived as minimally helpful. Homecare support was associated with negative overall experience. Least used resources included paid help, caregiver support groups and volunteer drivers but volunteer drivers were associated with less caregiver burden. Qualitative analysis revealed three themes (1) emotional labour of caregiving and respite from known contacts, (2) perception of formal resources as inappropriate for non-medical needs and (3) financial needs and role conflicts remain to be overcome. Informal caregivers are most likely to turn to known existing social networks for support as a result of accessibility and convenience, which are central to addressing most caregiver needs except for financial needs and role conflict. Future research should aim to remove barriers to resource utilization and strengthen existing support and resources. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".