Bibliographic record
Abstract
Purpose In Canada, community engagement and accountability are a political imperative, resulting in an omnipresent program with varied opportunities for public participation. The purpose of this paper is to promote leadership and commitment for health system transformation that truly benefits communities. Design/methodology/approach This paper is based on the author’s experience with many engagement and accountability activities, applied in varied settings, for purposes such as evaluation, planning, policy making and system transformation. The specific context is generalized with international experiences and references. Findings The “lessons learned” are based on practical considerations with relevance for both novice and experienced practitioners: clarifying principles, processes and purposes at the outset; using effective leadership to achieve the desired impact; using a variety of methods to engage communities; clarifying engagement and accountability roles precisely; measuring things that are meaningful; and consulting with internal as well as external communities. Also, community leaders should recognize effort as well as results. Research limitations/implications Commitment to engagement and accountability is commendable – but is it enough? The paper concludes by looking beyond health system impacts to propose a broader systems perspective. If clinical governors want to use engagement and accountability to achieve “total value” for their communities, they will need to demonstrate as leaders that they are committed to long-term thinking and broad social goals. Originality/value Too much focus on the process of care may mask accountability for reporting outcomes or systemic impact. The sustainable development goals highlight the need for systems thinking and public expectations include corporate social responsibility. As shown in the examples cited, a deeper commitment to engagement and accountability requires looking beyond care delivery to social determinants and to systemic impacts of the health care industry itself.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".