Policymaker perspectives on self-management of disease and disabilities using information and communication technologies
Bibliographic record
Abstract
Abstract Background Policies that support health self-management are malleable and highly dependent on various factors that influence governments. Within a world that is shifting toward digitalization due to pressures such as the COVID-19 pandemic and labor shortages, policymaking on older adults’ self-management of chronic diseases and disability using information and communication technologies (ICTs) needs to be better understood. Using the province of Ontario, in Canada, as a case study, the research question was What is the environment that policymakers must navigate through in development and implementation of policies related to older adults’ self-management of disease and disability using information and communication technologies (ICTs)? Methods This study used a qualitative approach where public servants from 4 ministries within the government of Ontario were invited to participate in a 1-h, one-on-one, semi-structured interview. The audio-recorded interviews were based on an adapted model of the policy triangle, where the researcher asked questions about the influences from the different sources identified in the model. The interviews were later transcribed and analyzed using a deductive-inductive coding approach. Results Ten participants across 4 different Ministries participated in the interviews. Participants shared insights on various aspects of context, process and actors that help shape the current content of policies. The analysis revealed that policies, in the form of programs, services, legislation and regulations, are the result of collaborations and dialogue between different actors and get developed and implemented via a set of complex government processes. In addition, policy actions come from a plethora of sectors which all get influenced by several predictable and unpredictable external pressures. Conclusions The environment for policymaking in the government of Ontario regarding older adults’ self-management of disease and disability using ICTs is one that is mostly reactive to external pressures, while organized within a set of complex processes and multi-sectoral collaborations. The present research helped us to understand the complexity of policymaking on the topic and highlights the need for increased foresight and proactive policymaking, regardless of which governments are in-place.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 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".