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. L’on a beaucoup insisté sur la responsabilité dans les soins de santé dans toutes les provinces et territoires du Canada. Par le biais de l’utilisation de l'analyse documentaire et des entretiens avec des informateurs clés, nous avons évalué la mesure dans laquelle les résultats de notre étude antérieure basée en Ontario, Approches à la responsabilité, s’appliquaient à travers le Canada. Mise en œuvre de la bonne façon, la responsabilisation peut améliorer la performance, améliorer l'expérience des patients et favoriser une utilisation plus efficace des ressources. Si elle est mal appliquée, elle peut gaspiller des ressources précieuses, créer des effets pervers et encourager le contournement du système. Les résultats de cette étude ont renforcé les conclusions antérieures. Nos répondants ont souligné qu'il était important de se concentrer sur les objectifs recherchés et les points de transition dans le système; ils ont souligné que les ressources et un leadership stable ont été la clé. Bien que les bonnes mesures soient indispensables, elles ne sont pas toujours disponibles; en conséquence, ce qui est facile à mesurer a tendance à être ce qui est rapporté. Les organisations sont également réticents à être tenus responsables pour ce qu'ils ne peuvent pas contrôler. Ils ont noté que trop d'organisations demandent trop d'indicateurs de formes trop nombreuses. Bien que cette situation soit particulièrement problématique pour les petits organisations, le problème ne les affecte pas exclusivement. À l'avenir, il sera important de rationaliser et prioriserles mesures à la base des rapports, d’assurer que les ressources suffisantes soient disponibles pour appuyer la redevabilité et d’éduquer les utilisateurs quant à la valeur des rapports sur les activités de reddition de comptes, en leur montrant qu'ils y trouveront leur compte. En outre, il est important d'encourager la coordination et le partage entre les multiples organisations qui demandent des informations similaires sous différentes formes. Enfin, il est important de veiller à ne pas perdre de vue ce qui difficilement mesurable.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.263 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.020 | 0.065 |
| Scholarly communication | 0.045 | 0.028 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.011 | 0.016 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".