Factors influencing treatment decision‐making for cancer patients in low‐ and middle‐income countries: A scoping review
Bibliographic record
Abstract
PURPOSE: In this scoping review, we evaluated existing literature related to factors influencing treatment decision-making for patients diagnosed with cancer in low- and middle-income countries, noting factors that influence decisions to pursue treatment with curative versus non-curative intent. We identified an existing framework for adult cancer developed in a high-income country (HIC) context and described similar and novel factors relevant to low-and middle-income country settings. METHODS: We used scoping review methodology to identify and synthesize existing literature on factors influencing decision-making for pediatric and adult cancer in these settings. Articles were identified through an advanced Boolean search across six databases, inclusive of all article types from inception through July 2022. RESULTS: Seventy-nine articles were identified from 22 countries across six regions, primarily reporting the experiences of lower-middle and upper-middle-income countries. Included articles largely represented original research (54%), adult cancer populations (61%), and studied patients as the targeted population (51%). More than a quarter of articles focused exclusively on breast cancer (28%). Approximately 30% described factors that influenced decisions to choose between therapies with curative versus non-curative intent. Of 56 reported factors, 22 novel factors were identified. Socioeconomic status, reimbursement policies/cost of treatment, and treatment and supportive care were the most commonly described factors. CONCLUSIONS: This scoping review expanded upon previously described factors that influence cancer treatment decision-making in HICs, broadening knowledge to include perspectives of low- and middle-income countries. While global commonalities exist, certain variables influence treatment choices differently or uniquely in different settings. Treatment regimens should further be tailored to local environments with consideration of contextual factors and accessible resources that often impact decision-making.
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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 | 0.021 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".