Co-constructing Performance Indicators in Home and Community Care: Assessing the Role of NGOs in Three Canadian Provinces
Bibliographic record
Abstract
This article examines the extent to which public servants interact with non-governmental organizations (NGOs) to co-construct performance indicators in the home- and community-care sector. It uses 32 intensive qualitative interviews with NGO representatives and public servants in three Canadian provinces (British Columbia, Saskatchewan, and Ontario) with distinctive home- and community-care systems to uncover the lived experiences of NGO/government interactions around this issue and seeks to gain a greater understanding of the role of NGOs in shaping permanence indicators. Varying funding and delivery models of home and community care across provinces put NGOs in different roles in the delivery of home and community supports, and hence, set different contexts for NGO/public servant interactions across the three provinces. Cet article examine dans quelle mesure les fonctionnaires publics interagissent avec les organisations non gouvernementales (ONG) pour établir des indicateurs de performance dans les secteurs des soins à domicile et de proximité. Il se base sur 32 entretiens en profondeur avec des représentants d’ONG et des fonctionnaire publics dans trois provinces canadiennes (Colombie-Britannique, Saskatchewan et Ontario) ayant des systèmes distincts de soins à domicile et de proximité, et ce afin d’en apprendre davantage sur la réalité des interactions entre les ONG et le gouvernement. L’article cherche en outre à mieux comprendre le rôle des ONG dans la formulation des indicateurs de performance. Les divers modèles dans chaque province pour financer et offrir des soins à domicile et de proximité ont un impact sur la manière dont les ONG peuvent fournir leur aide à domicile et dans la communauté, et créent ainsi des contextes différents dans chacune des trois provinces pour les interactions entre les ONG et les fonctionnaires publics.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".