Implementation of self-management support in cancer care and normalization into routine practice: a systematic scoping literature review protocol
Bibliographic record
Abstract
BACKGROUND: Cancer survivors face a myriad of biopsychosocial consequences due to cancer and treatment that may be potentially mitigated through enabling their self-management skills and behaviors for managing illness. Unfortunately, the cancer system lags in its systematic provision of self-management support (SMS) in routine care, and it is unclear what implementation approaches or strategies work to embed SMS in the cancer context to inform health policy and administrator decision-making. METHODS/DESIGN: A comprehensive scoping review study of the literature will be conducted based on methods and steps identified by Arksey and O'Malley and experts in the field. Electronic searches will be conducted in multiple databases including CINAHL, CENTRAL, EMBASE, PsycINFO, MEDLINE, AMED, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE) (up to Issue 2, 2015), ISI Proceedings (Web of Science), PsychAbstracts, and Sociological Abstracts from January 1997 to November 5, 2018. Following the PRISMA-Extension for Scoping Reviews (PRISMA-ScR), two authors will independently screen all titles/abstracts to determine eligibility, data will be abstracted by one author and checked by a second author, and findings will be narratively summarized based on constructs of implementation in the Normalization Process Theory. DISCUSSION: This will be the first scoping review study to synthesize knowledge of implementation of SMS in the cancer care context and the implementation approaches and strategies on embedding in care. This information will be critical to inform health policy and knowledge end users about the necessary changes in care to embed SMS in practices and to stimulate future research.
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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 | 0.133 | 0.101 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".