Poster Introductions III--The Lifecourse of Esophageal Cancer Patients Traced by Means of the Lifegrid
Bibliographic record
Abstract
Background: The absence of prospective longitudinal studies for certain health outcomes creates the need to collect accurate information retrospectively. In an attempt to minimize recall bias and to better understand the deeper rooted issues involved in the risk factors for esophageal cancer, the adoption of the lifegrid accompanied by supplementary tools was implemented. The objective was to provide a more comprehensive and context-sensitive perspective to the study of the living and working environments of esophageal cancer patients across the lifecourse.\nMethods: A sample of 46 esophageal cancer patients were recruited from participating London and Toronto hospitals. This study involved the completion of face-to-face interviews guided by a semistructured questionnaire, a lifegrid, occupational and residential summary boxes, residential pictures and occupational risk maps, and reference to other supplementary information and sources of evidence.\nFindings: The utilization of the life grid allowed for a holistic interpretation of the data allowing for a visual analysis of various factors at play across the lifecourse. The use of the lifegrid retained the temporal ordering of the lifecourse, made it easy to identify and probe for transitions and trajectories, and allowed for various links across factors to be made during data collection. Nevertheless, its use also produced a number of challenges such as lengthy interviews, event-centered data, and the inability to pinpoint specific unknown exposures. However, these limitations could be overcome by conducting multiple, short interviews, making modifications after pilot interviews, incorporating supplementary tools, and referencing sources of evidence.\nConclusions: The lifegrid aids in stimulating recall in a factual fashion. The versatility of the lifegrid allows it to be easily modified based on different population groups and research objectives. With the integration of other supplementary tools, the adoption of the lifegrid is recommended as a methodological tool to guide other research pertaining to lifecourse studies.\nAnn Novogradec is a Ph.D. Candidate in the Faculty of Environmental Studies at York University. Her dissertation research, part of which is being presented in this poster, investigated the changing living and working environments that esophageal cancer patients experienced and were exposed to across their lifecourse. It utilized the lifegrid to aid in this objective. Her research interests include: esophageal cancer; cancer and the environment; environment and health; ecosystem health; occupational health and disease; social, cultural, economic, and political interface; cancer prevention strategies; life review; mixed methods; holistic research frameworks and strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".