Coping Profiles of Cancer Patients With Different Functional and Psychosocial Status: A Person-Oriented Approach
Bibliographic record
Abstract
learning, networking, collaboration, “healthy” competition, social, and financial support. Through the reliance on information technology, face-to-face exchanges, dedicated workshops and research internships, PORT’s program offers varied and innovative research skills building activities which have been throughout the years instrumental in shaping the developing career of its trainees. RESEARCH IMPLICATIONS: In a field as competitive as research, an excellent training experience is invaluable in building capacity. Research training programs such as PORT enhance the development of skills and competencies to enable fellows to design and carry-out innovative, high quality, person-centred, and feasible studies. CLINICAL IMPLICATIONS: Supporting aspiring young researchers to think outside the box, design timely studies, innovate in their field, and proactively disseminate their results can directly contribute to enhancing clinical practice. The knowledge gained through such training programs set the conditions and contexts that most favorably launch junior researchers into an exciting career. ACKNOWLEDGEMENT OF FUNDING: Julie Lapointe is currently a postdoctorate CIHR Fellow in PORT a Strategic Training Initiative in Health Research (STIHR) funded by the Canadian Institutes of Health Research (CIHR). Fay Strohschein has received funding from the FRQ-S; the Quebec Network for Research on Aging, the PORT Program; the McGill University Faculty of Medicine; and the Jewish General Hospital Department of Nursing. Shannon Groff is funded through the Alberta Cancer Foundation, the PORT Program, Knowledge Translation Canada and the CIHR.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".