A protocol for determining differences in consistency and depth of palliative care service provision across community sites
Bibliographic record
Abstract
Numerous accounts document the difficulty in obtaining accurate data regarding the extent and composition of palliative care services. Compounding the problem is the lack of standardisation regarding categorisation and reporting across jurisdictions. In this study, we gathered both quantitative and spatial--or geographical--data to develop a composite picture that captures the extent, composition and depth of palliative care in the Canadian province of British Columbia (BC). The province is intensely urban in the southwest and is rural or remote in most of the remainder. For this study, we conducted a detailed telephone survey of all palliative care home care teams and facilities hosting designated beds in BC. We used geographic information systems to geocode locations of all hospice and hospital facilities. In-home care data was obtained individually from each of five BC regional health authorities. In addition, we purchased accurate road travel time data to determine service areas around palliative facilities and to determine populations outside of a 1-hour travel time to a facility. With this data, we were able to calculate three critical metrics: (i) the population served within 1 hour of palliative care facilities--and more critically those not served; (ii) a matrix that determines access to in-home palliative care measured by both diversity of professionals as well as population served per palliative team member; and (iii) a ranking of palliative care services across the province based on physical accessibility as well as the extent of in-home care. In combination, these metrics provide the basis for identifying areas of vulnerability with respect to not meeting potential palliative care need. In addition, the ranking provides a basis for rural/urban comparisons. Finally, the protocol introduced can be used in other areas and provides a means of comparing palliative care service provision amongst multiple jurisdictions.
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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 | 0.071 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.031 |
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".