Planning without action and action without planning? Examining a regional health system's efforts to improve patient flow, 1998–2013
Bibliographic record
Abstract
Most health care organizations engage in formal and informal planning, yet their improvement initiatives may remain disjointed and reactive. Research on organizational decision-making has found that the "discovery" approach (seek and assess multiple options before selecting one) outperforms "idea imposition" (identify 1 option, then gather information to [dis]confirm it), yet is observed relatively infrequently. Might this imply that discovery frequently collapses before fruition? This qualitative study sought to better understand the planning-action disjunction, as observed in 1 organization, by comparing its planning processes against the discovery approach. It focused on a Canadian regional health system's recurrent, unsuccessful attempts to improve patient flow. Through extensive document review supplemented by interviews with 62 managers, it identified all relevant regional plans/reports produced during a 15-year period and followed each recommendation forward in time to discover its fate. Each report presented a lengthy, unprioritized list of disparate recommendations, few of which progressed to full implementation. It appeared that decision-makers repeatedly embarked on a discovery approach, but rapidly allowed it to splinter into multiple idea-imposition approaches; numerous options were generated, but never evaluated against each other. Thus, the product of each planning process was not a coherent strategy but a list of disconnected actions.
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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 | 0.036 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".