Fit theory: A cancer experience grounded theory emerging from semi-structured interviews with cancer patients and informal caregivers in Manitoba Canada during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: It is not clear how changes to healthcare delivery related to the COVID-19 pandemic, including virtual care and social distancing restrictions, have impacted the experience of living with cancer. This study aimed to discover a theory capable of describing the cancer experience, how the pandemic impacted it, and for guiding predictions about how to improve it. METHODS: Between October 2020 and July 2021 digitally recorded semi-structured one-on-one interviews were conducted virtually with adult cancer patients and informal caregivers in Manitoba, Canada. Transcriptions and field notes from the interviews were analyzed using classic grounded theory. RESULTS: Interviews with 33 patients and 6 informal caregivers were conducted. Fit emerged as the core concept of the theory and describes the relationship between the healthcare system and the unique combination of characteristics each patient has. Good fit results in a positive experience and poor fit in a negative experience. Virtual care improves fit in clinical situations where non-verbal communication and physical examination are not important. Support from informal caregivers improves fit. Social distancing restrictions reduce the ability of informal caregivers to provide support. CONCLUSIONS: The impact of fit on the cancer experience suggests that care delivery should be tailored to both the individual needs of the patient and the intention of the clinical interaction. Developing evidence-based strategies to inform the integration of virtual care into oncology practice, with aim of promoting good fit between patients and healthcare services, is an important future direction.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".