Barriers to providing palliative care to patients with advanced cancer: A province-wide survey of oncology clinicians’ perceptions.
Bibliographic record
Abstract
88 Background: Despite known benefits, cancer care systems struggle to provide early, integrated palliative care (PC). Previously, we identified barriers to providing early PC as perceived by gastrointestinal oncology clinicians in Alberta, Canada (top barrier: time/competing priorities). Here, we expand on the previous study to better understand barriers to early PC for clinicians working with all tumor groups across Alberta. Methods: A 33-item survey was emailed to oncology clinicians in Alberta between November 2017 - January 2018. Questions were informed by Michie’s Theoretical Domains Framework (TDF) and Behaviour Change Wheel (BCW) and queried (a) providing PC in oncology clinics, (b) referral to specialist PC consultation, and (c) working with PC consultants and homecare. Results: Respondents (n = 268) were nurses (42%), physicians (25%), and allied health professionals (20%). Barriers most frequently identified were "patients’ negative perceptions of PC” (68%), “my limited time/competing priorities” (66%), and "capability to manage patients’", social (65%) and spiritual (63%) concerns. These factors map to all three BCW domains: motivation, opportunity, and capability. In contrast, least frequently identified barriers were in clinician’s own motivation, e.g. perceived benefits of PC. There were few significant differences in response by tumor group or profession (χ2 test, responses coded: disagree [1-3], neutral [4], agree [5-7]). Most notably, tumor groups differed in their perception that “the criteria for PC services are too restrictive” (p = 0.003), while nurses and allied staff reported that patients’ negative perception of PC is a barrier more frequently than physicians (p = 0.003). Conclusions: Surveying across clinicians and tumor groups using Michie’s TDF/BCW revealed that the challenges to an early integrated PC approach include all three sources of behavior, though not equally for all clinicians. Determining this has allowed us to tailor multifaceted interventions, e.g. tip sheets to enhance capability, re-framing PC with patients, and earlier secondary PC nursing access, to enhance clinicians use and patients benefit from an early PC approach.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".