Understanding and Implementing Best Practices in Accountability
Bibliographic record
Abstract
There has been much emphasis on accountability in health care in all jurisdictions across Canada. Using document analysis and key informant interviews, we assessed the extent to which the findings from our earlier Ontario-based study, Approaches to Accountability, applied across Canada. Accountability done well improves performance, improves the patient experience and promotes efficient use of resources. If implemented poorly, it can waste valuable resources, create perverse incentives and encourage gaming in the system. The findings of this study reinforced the earlier findings. Our respondents stressed that it was important to focus on the goals being sought and transition points in the system; they emphasized that resources and stable leadership were key. Although good metrics are essential, they are not always available. Accordingly, what is easily measured tends to be what is reported. Organizations are also reluctant to be held accountable for what they cannot control. They noted that too many organizations are asking for too many indicators in too many forms. Although this is particularly problematic for small organizations, it is not exclusive to them. Moving forward, it will be important to streamline and prioritize reporting metrics, ensure adequate resources are available to support accountability and educate users as to the value of reporting accountability activities, by showing them that there is something in it for them. In addition, it is important to encourage coordination and sharing among the multiple bodies that request similar information in different forms. Finally, it is important to ensure that that which is difficult to measure is not lost in the shuffle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.248 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.043 | 0.023 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".