A method to determine spatial access to specialized palliative care services using GIS
Bibliographic record
Abstract
BACKGROUND: Providing palliative care is a growing priority for health service administrators worldwide as the populations of many nations continue to age rapidly. In many countries, palliative care services are presently inadequate and this problem will be exacerbated in the coming years. The provision of palliative care, moreover, has been piecemeal in many jurisdictions and there is little distinction made at present between levels of service provision. There is a pressing need to determine which populations do not enjoy access to specialized palliative care services in particular. METHODS: Catchments around existing specialized palliative care services in the Canadian province of British Columbia were calculated based on real road travel time. Census block face population counts were linked to postal codes associated with road segments in order to determine the percentage of the total population more than one hour road travel time from specialized palliative care. RESULTS: Whilst 81% of the province's population resides within one hour from at least one specialized palliative care service, spatial access varies greatly by regional health authority. Based on the definition of specialized palliative care adopted for the study, the Northern Health Authority has, for instance, just two such service locations, and well over half of its population do not have reasonable spatial access to such care. CONCLUSION: Strategic location analysis methods must be developed and used to accurately locate future palliative services in order to provide spatial access to the greatest number of people, and to ensure that limited health resources are allocated wisely. Improved spatial access has the potential to reduce travel-times for patients, for palliative care workers making home visits, and for travelling practitioners. These methods are particularly useful for health service planners - and provide a means to rationalize their decision-making. Moreover, they are extendable to a number of health service allocation problems.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".