Public sector capacity to plan and deliver public/private infrastructure partnerships (P3s): a case study of British Columbia’s healthcare sector
Bibliographic record
Abstract
Sweeping public sector reforms have occurred globally in recent decades. One of the more high profile and contentious strategies pursued in jurisdictions such as Australia, the United Kingdom, and more recently Canada is the radical shift to public-private infrastructure partnerships - commonly referred to in Canada as ‘P3s’. The Government of British Columbia (BC) has emerged as the leading proponent of the P3 model in Canada. The strategic shift in BC to the P3 model was introduced in 2002 by the governing Liberal party. The Liberals created Partnerships BC to spearhead the P3 program, and singled out the healthcare sector to move forward the P3 agenda. The dynamics of delivering a complex and unfamiliar business model like P3s through this new public agency, combined with the unique ecology of the healthcare system, added to the complexity of delivering a central government mandated strategic policy priority like a P3-first agenda, making for a robust case study. Focusing on the issue of public sector capacity to implement policy, this study examines how BC has transitioned from traditional procurement and operation of public infrastructure such as hospitals to the wide-spread use of P3s. The study asks questions about three independent variables of capacity related to implementation of P3 and other cross-cutting programs: governance and oversight; human resources; and, inter-agency collaboration within the public sector. Drawn from an extensive review of scholarly studies and literature, and government and consultant reports examining actual infrastructure P3 experiences and exemplary practices, these variables formed the basis of the study’s analytical model. The study employed the following multi-method (triangulation) research and data collection approach: i) an archival review of text-based government documents; ii) interviews with key public employees either directly or indirectly involved in P3 program implementation; and, iii) field observations of the principal researcher based on first-hand experiences as a senior manager in the BC government during this period. Based on the analytical model developed, the study reveals a capacity deficit in BC to adequately plan and implement a comprehensive P3 program. Based on the literature, the model establishes that central agencies play a critical role in an effective P3 policy program. So what is most noteworthy is the lack of resources and attention given to developing central agency capacity to: advance sound P3 policies; ensure ongoing program oversight and accountability; and, provide necessary guidance and support to agencies tasked with implementing very complex P3 arrangements in a multi-agency domain. For practitioners, the study validates key elements of capacity to be considered when planning and implementing major shifts in public policy. The literature review and the study’s findings also have relevance for the public administration profession through the development of the analytical framework and its application to an important contemporary public policy area. As a contribution to scholarly and academic research, this paper tests the validity and reliability of the analytical model developed and the overall methodology used, and identifies significant areas that merit further study related to the broader phenomenon of P3s and public sector capacity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".