Infrastructure to support modern primary care: the NAPCRG debate update
Bibliographic record
Abstract
In December 2012, at the North American Primary Care Research Group (NAPCRG) conference in New Orleans (USA), associates of London Journal of Primary Care (LJPC) from London (UK) and Cleveland Ohio (USA) hosted a forum entitled ‘Local Health Communities for Integrated Care’. The forum was attended by senior practitioners/academics from different countries who debated the infrastructure needed to support collaboration at primary/community care level. They drew on lessons from West London's integrated care pilot1 and Cleveland's ‘Promoting Health across Boundaries’ initiative.2 Participants at the forum recognised international need for case studies of community-oriented integrated care (COIC), including primaryand community-care leadership of collaboration for integrated care, and continuous improvements of whole systems of care. In November 2013, at the NAPCRG conference in Ottawa (Canada), LJPC associates from Quebec (Canada) and London (UK) hosted a second forum to continue this conversation. In preparation for the Ottawa Forum, in October 2013 LJPC invited its readers to rank 11 aspects of integrated care in respect of their importance to research. Table 1 shows the results in order of most ‘votes’ overall (weighted formula). It also shows the first four (of 11) ‘votes’ of 61 respondents. Table 2 shows additional comments made by 30/61 respondents. Table 1 Priority aspects of integrated care to research Table 2 Additional comments made by 30 respondents about researching integrated care While ‘Patient Involvement’ and ‘Extended Multidisciplinary Primary Care Teams’ were perceived to be the most important aspects (and indeed 29/61 respondents gave them their first ‘vote’), the overall ranking pattern is more complicated. There is an argument for saying that all of these aspects are important, and their perceived importance may relate to their perceived neglect at present. Recognising the wide range of qualitative comments offered in Table 2, these initial results illustrate the range of research still needed to understand how the infrastructure of primary care can be developed, and, as discussions at the two conferences elaborated, more progress can be made in developing research that examines primary care in context.
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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 | 0.042 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.045 | 0.031 |
| Insufficient payload (model declined to judge) | 0.021 | 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".