When rules turn into tools: An activity theory-based perspective on implementation processes and unintended consequences
Bibliographic record
Abstract
The idea that actions of people, organizations or governments may lead to Unintended Consequences (UICs) is not new. In health, UICs have been reported as a result of various interventions including quality improvement initiatives, health information technology implementation, and knowledge translation, especially those involving translation of broad policies (evidence-based medicine and patient-centred care) or system level improvement into actionable items or tools. While some unintended consequences cannot be anticipated, others may be predictable. In this article, we present a model based on cultural historical activity theory, which may help policy-makers, health leaders, and researchers better anticipate UICs resulting from implementation of new programs or technologies and take action to address them or mitigate their risk of occurrence. We support this model using examples of UICs of implementing family centred care principles, electronic health records, and computerized templates for quality improvement in chronic disease management.
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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.062 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".