Challenges of Dealing with Financial Concerns during Life-Threatening Illness: Perspectives of Health Care Practitioners
Bibliographic record
Abstract
The costs of serious medical illness and end of life care are often a heavy burden for patients and families (Collins, Stepanczuk, Williams, & Rich, 2016 Collins, A., Stepanczuk, C., Williams, N., & Rich, E. (2016). Supporting better patient decisions at the point of care: What payers and delivery systems can do (Mathematica Policy Research Issue Brief). Retrieved from http://econpapers.repec.org/paper/mprmprres/6c6a86e28d7149c993713352eeceaa18.htm [Google Scholar]; Kim, 2007 Kim, P. (2007). Cost of cancer care: The patient perspective. Journal of Clinical Oncology, 25(2), 228–232. doi:10.1200/JCO.2006.07.9111[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; May et al., 2014 May, C. R., Eton, D. T., Boehmer, K., Gallacher, K., Hunt, K., MacDonald, S., … & Rogers, A. E. (2014). Rethinking the patient: Using burden of treatment theory to understand the changing dynamics of illness. BMC health services research, 14(1), 1–11. doi:10.1186/1472-6963-14-281[Crossref], [PubMed] , [Google Scholar]; Zarit, 2004 Zarit, S. H. (2004). Family care and burden at the end of life. Canadian Medical Association Journal, 170(12), 1811–1812. doi:10.1503/cmaj.1040196[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Twenty-six practitioners, including social workers, managers/administrators, supervisors, and case managers from five health care settings, participated in qualitative semistructured interviews about financial challenges patients encountered. Seven practitioners took part in a focus group. Practitioners were recruited from hospice (n = 5), long-term care (n = 5), intensive care (n = 5), dialysis (n = 6), and oncology (n = 5). Interview and focus group questions focused on financial challenges patients encountered when facing life-threatening illness. Interview data were transcribed and thematically coded and trustworthiness of data was established with peer debriefing, member checking, and agreement on themes among the authors. Practitioners described interacting micro, meso, and macroinfluences on the financial well-being and challenges patients encountered. Microlevel influences involved patient characteristics, such as their demographic profile and/or health status that set them up for financial aptitude or challenges. Macrolevel influences involved the larger health care/safety net system, which provided valuable resources for some patients but not others. Practitioners also discussed the mesolevel of influence, the local setting where they worked to match available resources with patients’ individual needs given the constraints emerging from the micro and macrolevels. Practitioners described how they navigated the interplay of these three areas to meet patients’ needs and cope with financial challenges. Implications for practice point to directly addressing the kind of financial concerns that patients and families facing financial burden from serious medical illness have, and identifying ways to bridge knowledge and resource access gaps at the individual, organizational, and societal levels.
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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 | 0.052 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".