Policymaking in Ontario for Older Adults' Self-Management of Disease and Disability Using Information and Communication Technologies
Bibliographic record
Abstract
Policymaking on health issues has evolved since the early 2000s. Since then, there have been significant changes in society including increased aging of the population and the emergence of technology. However, the evolution of policymaking and current approaches to policymaking cross-cutting in those areas are not well documented. The purpose of this dissertation is to explore the factors that influence the development and implementation of a framework for policymaking specific to the topic of older adults’ self-management of disease and disabilities using information and communication technologies (ICT). I conducted one large study with three phases, using Ontario as a case study, to document the history and portray current approaches to policymaking in this area, and finally, to develop and validate a conceptual framework for policymaking. Grounded in Walt and Gilson’s model for policy analysis, we performed the following: Phase 1, which is presented in the first manuscript entitled ‘Evolution of public health policy on healthcare self-management: the case of Ontario, Canada’, used archival research methods combining document review and evaluation to identify specific factors that have influenced the development of healthcare self-management policies over time in Ontario. Seventy-two documents on self-management of health were retrieved, analyzed, and evaluated using Walt and Gilson’s model for policy analysis. Findings suggested that self-management of health was introduced into policies in the early 2000s with a focus on diabetes and chronic conditions. Very few policies had a specific focus on older adults, and technology was introduced as a tool to support self-management in the context of healthcare delivery and enhancing healthcare infrastructure. Additionally, the factors that have influenced the evolution of policies over time include pressures on the healthcare system, hybrid top-down and bottom-up policymaking, and the political context. Phase 2, which is presented in the second manuscript entitled ‘Policymaker perspectives on self-management of disease and disabilities using information and communication technologies’ used a qualitative approach to identify the current environment for policymaking on the topic older adults’ self-management of disease and disability using ICTs. Ten policymakers from four different ministries in the Government of Ontario participated in semi-structured interviews. The analysis revealed that policies, in the form of programs, services, legislation and regulations, are the result of collaboration and dialogue between different actors and via a set of complex government processes. In addition, policy actions come from a plethora of sectors which all get influenced by predictable and unpredictable external pressures. The environment for policymaking was found to be mostly reactive to external pressures, while organized within a set of complex processes and collaborations. Phase 3, which is presented in the manuscript entitled ‘Validation of a framework for policymaking on self-management of health by older adults using technologies’ used a quantitative approach to validate a provisional framework for policymaking. Nine participants answered the survey questions, and some offered unsolicited insights. The results revealed that the provisional framework adequately captured the areas that should compose a framework for policymaking for older adults’ self-management of disease and disability using ICTs in Ontario. Participants noted the need to further explore and define certain concepts of the provisional framework, and that other relevant concepts should be added to the framework. The study proposes a refined framework which offers an opportunity for policymakers to advance the policy agenda for older adults’ self-management of disease and disability using ICTs. Policymakers working in the realm of older adults’ self-management of disease and disability are faced with a rapidly evolving and changing context where technologies are becoming omnipresent. With policies often lagging the consideration and inclusion of technology, a framework that supports a coordinated and holistic approaches to policymaking can enhance proactivity and cohesive actions from governments to consider their application. This can lead to better health outcomes for older adults living in this innovative and everchanging ecosystem.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".