Use of Performance Data by Mid-Level Hospital Managers in Ontario: Results of a Province-Wide Survey and a Comparison with Hospital Managers in Europe
Bibliographic record
Abstract
This paper provides insights into the use of performance data by middle managerial staff in Ontario hospitals in 2019 and compares the results to a study conducted in Europe in the same year.A total of 236 managers working in 61 hospitals across Ontario provided responses to the survey.Compared to their European colleagues, Ontario respondents self-assessed using significantly more performance data for managerial decision making.The use of performance data in Ontario was mostly motivated by external accountability requirements, followed by internal quality improvement efforts.Ontario managers also reported accessibility, appropriateness and timeliness of data and human resources and engagement as the biggest barriers to further performance data utilization.Comparative studies, such as the one this paper is based on, provide the foundation for drawing lessons across jurisdictions.This paper also affirms the importance of hospital middle management in moving from quality assurance to quality improvement efforts and developing sustainable learning healthcare organizations and systems. RésuméCet article donne un aperçu de l' utilisation des données sur le rendement par le personnel de gestion intermédiaire dans les hôpitaux de l'Ontario en 2019 et compare les résultats à une étude menée en Europe la même année.En tout, 236 gestionnaires œuvrant dans 61 hôpitaux ontariens ont répondu au sondage.Comparativement à leurs collègues européens, les répondants ontariens déclarent utiliser beaucoup plus de données sur le rendement pour la prise de décisions en matière de gestion.L' utilisation des données sur le rendement en Ontario est principalement motivée par les exigences externes en matière de reddition de comptes, suivies d' efforts internes d' amélioration de la qualité.Les gestionnaires ontariens indiquent également que l' accessibilité, la pertinence et l' actualité des données, des ressources humaines et de l' engagement étaient les principaux obstacles à une utilisation plus poussée des données sur le rendement.Des études comparatives, telles que celle sur laquelle se fonde le présent document, fournissent la base pour tirer des leçons entre les juridictions.Cet article affirme également l'importance de la gestion intermédiaire hospitalière dans le passage de l' assurance de qualité vers les efforts d' amélioration de la qualité ainsi que dans le développement d' organisations et de systèmes de santé d' apprentissage durables.T
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".