New ways to get policy into practice
Bibliographic record
Abstract
PURPOSE: Health service effectiveness continues to be limited by misaligned objectives between policy makers and frontline clinicians. While capturing the discretion workers inevitably exercise, the concept of "street-level bureaucracy" has tended to artificially separate policy makers and workers. The purpose of this paper is to understand the role of social-organizational context in aligning policy with practice. DESIGN/METHODOLOGY/APPROACH: This mixed-method participatory study focuses on a locally developed tool to implement an Australia-wide strategy to engage and respond to mental health services for parents with mental illness. Researchers: completed 69 client file audits; administered 64 staff surveys; conducted 24 interviews and focus groups (64 participants) with staff and a consumer representative; and observed eight staff meetings, in an acute and sub-acute mental health unit. Data were analyzed using content analysis, thematic analysis and descriptive statistics. FINDINGS: Based on successes and shortcomings of the implementation (assessment completed for only 30 percent of clients), a model of integration is presented, distinguishing "assimilist" from "externalist" positions. These depend on the degree to which, and how, the work environment affords clinicians the setting to coordinate efforts to take account of clients' personal and social needs. This was particularly so for allied health clinicians and nurses undertaking sub-acute rehabilitative-transitional work. ORIGINALITY/VALUE: A new conceptualization of street-level bureaucracy is offered. Rather than as disconnected, it is a process of mutual influence among interdependent actors. This positioning can serve as a framework to evaluate how and under what circumstances discretion is appropriate, and to be supported by managers and policy makers to optimize client-defined needs.
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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 | 0.115 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.100 |
| Scholarly communication | 0.044 | 0.062 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.026 | 0.031 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".