CANO/ACIO CLINICAL LECTURESHIP 2014 Preliminary data on the lived experience of having multiple primary cancer diagnoses
Bibliographic record
Abstract
Approximately one in two Canadians will develop some form of cancer, and some will live long enough to be diagnosed with multiple primary cancers. There is some indication that multiple primary cancer diagnoses negatively impact survivors’ mental and physical status, and quality of life. Existing research studies do not fully capture the complexity of what it is like to have multiple primary cancer diagnoses. Accordingly, a qualitative study was conducted to elicit detailed descriptions of the lived experiences of having multiple primary cancer diagnoses. Participants included 10 individuals from Atlantic Canada with a history of two or more cancer diagnoses. Data were captured through semi-structured interviews and participant-generated photographs. Interviews were transcribed and reviewed for common meanings. Preliminary data analyses suggest that the essential meaning of having cancer multiple times is that cancer is “unwanted encore”. This study yields findings that can provide empirically-based guidance to healthcare providers to help support cancer survivors in a more holistic way throughout the extended continuum of care and ultimately improve the health of individuals who have had multiple primary cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.006 |
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".