Defining and implementing early palliative care for persons diagnosed with a life-limiting chronic illness: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This review will explore definitions of early palliative care and describe how it has been implemented for those diagnosed with a life-limiting chronic illness. INTRODUCTION: People with life-limiting chronic illnesses who receive palliative care interventions have increased quality of life, better symptom management, and are more likely to have advance care plans than patients who do not have life-limiting chronic illness. It is therefore best practice to encourage early identification of persons in need of palliative care services. However, there is uncertainty over what is considered to be "early palliative care" and this presents a barrier to evaluating associated outcomes. INCLUSION CRITERIA: All literature that defines an early palliative care approach in adults (aged 18 years and older) with a life-limiting chronic illness in any health care setting will be included in this review. All countries and sociocultural settings will be included. METHODS: This scoping review will follow JBI methodology. A comprehensive search of academic and gray literature using MEDLINE (Ovid), CINAHL (EBSCO), Embase (Ovid), PsycINFO (Ovid), Web of Science Core Collection, Ovid Cochrane Library, and ProQuest (Health and Medicine and Sociology Collections) will be utilized. Articles will be screened for inclusion by two independent reviewers. Results will be extracted using a customized tool and summarized in a final report using a narrative synthesis presented in table form.
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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 | 0.101 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".