Is there a role for hybrid service provision in place‐based initiatives within the human services sector? Findings from an Australian exploratory study
Bibliographic record
Abstract
Abstract The value of place‐based initiatives in the design and delivery of human services has long been recognised, but the need for hybrid service delivery to clients—that is any combination of online and in‐person modalities—has become more apparent in the wake of the COVID‐19 pandemic. At face value, there may be a perceived contradiction between the reliance on geographical place in place‐based initiatives and online service delivery. Yet, it is inevitable that human services will increasingly be delivered in a hybrid form—even in the context of place‐based initiatives. This exploratory study included a modified Delphi method and deliberative panels with policymakers, service providers, and academics with experience and/or knowledge of hybrid place‐based initiatives. A central finding was the lack of respondents’ shared understanding and ability to link the three central intersecting ideas being examined in this research: ‘hybrid’ and ‘place‐based’ and ‘human services’. The principles underpinning place‐based initiatives need to be retained when incorporating online service delivery within place‐based initiatives. This reflects the need to develop a shared lexicon on hybrid place‐based initiatives and more creative understandings and interpretations of the relationship between people and place. Points for practitioners Increasingly, human services are being designed, implemented, and delivered through place‐based initiatives, yet the success of place‐based initiatives often relies on more than just face‐to‐face service delivery. The COVID‐19 pandemic shifted many human services online, but how this has impacted on place‐based initiatives and their defining features of co‐location, collaboration, and co‐design has not been explored to date. Following the pandemic, both service providers and clients see the benefit in retaining and integrating face‐to‐face and online services, without understanding how best to achieve this with place‐based initiatives. This study draws on practices developed during the pandemic to understand how to best provide hybrid services (integrating in‐person and online services) and shows the relevance of hybrid services to place‐based initiatives and considerations moving forward.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".