A conceptual basis for strategic health planning in governments in Canada
Bibliographic record
Abstract
This study presents a new conceptual model of health planning for planners in government. The model developed in this study is based on concepts from the "sociology of knowledge," a branch of sociological theory. There is currently no universally agreed upon grand theory of health planning. The contribution of the sociology of knowledge is that it allows the analyst to move to a higher level of abstraction in which existing theories or models themselves become the subjects of study. This type of formulation allows the theorist to incorporate existing theories into a conceptual space based on fundamental conceptions of the world. By understanding the location of a planning project in this conceptual space, the planner can identify the sets of theories most appropriate to the project, i.e. a contingency model allowing for the best fit resolution matching a project to its correct model of analysis. Knowing which theories to use allows the planner to develop strategies for action. The model in this study has three components: analysis of the planning environment or context, strategy formulation, and the action required to carry out the planning project. This model is called the Contextual Analysis, Strategy and Action (CASA) model. The validity of this model is tested by applying it to six case studies. The conclusion of the study is that the CASA model is a valid model and has more explanatory power than other documented models of planning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".