Community Health Care Workers’ Experiences on Enacting Policy on Technology with Citizens with Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
Torhild Holthe,1 Liv Halvorsrud,2 Erik Thorstensen,3 Dag Karterud,2 Debbie Laliberte Rudman,4 Anne Lund1 1Oslo Metropolitan University, Faculty of Health Sciences, Department of Occupational Therapy, Prosthetics and Orthotics, Oslo, Norway; 2Oslo Metropolitan University, Faculty of Health Sciences, Department of Nursing and Health Promotion, Oslo, Norway; 3Oslo Metropolitan University, Work Research Institute, Centre for Welfare and Labour Research, Oslo, Norway; 4University of Western Ontario, School of Occupational Therapy & Graduate Program in Health and Rehabilitation Sciences, London, CanadaCorrespondence: Torhild HoltheOslo Metropolitan University, Faculty of Health Sciences, Department of Occupational Therapy, Prosthetics and Orthotics, PO Box 4 St. Olavs Plass, Oslo 0130, NorwayTel +47 911 34 088Email torhol@oslomet.noPurpose: Assistive technologies and digitalization of services are promoted through health policy as key means to manage community care obligations efficiently, and to enable older community care recipients with mild cognitive impairment (MCI) and dementia (D) to remain at home for longer. The overall aim of this paper is to explore how community health care workers enacted current policy on technology with home-dwelling citizens with MCI/D.Participants and Methods: Twenty-four community health care workers participated in one of five focus group discussions that explored their experiences and current practices with technologies for citizens with MCI/D. Five researchers took part in the focus groups, while six researchers collaboratively conducted an inductive, thematic analysis according to Braun & Clarke.Results: Two main themes with sub-themes were identified: 1) Current and future potentials of technology; i) frequently used technology, ii) cost-effectiveness and iii) “be there” for social contact and 2) Barriers to implement technologies; i) unsystematic approaches and contested responsibility, ii) knowledge and training and iii) technology in relation to user-friendliness and citizen capacities.Conclusion: This study revealed the complexity of implementing policy aims regarding technology provision for citizens with MCI/D. By use of Lipsky’s theory on street-level bureaucracy, we shed light on how community health care workers were situated between policies and the everyday lives of citizens with MCI/D, and how their perceived lack of knowledge and practical experiences influenced their exercise of professional discretion in enacting policy on technology in community health care services. Overall, addressing systematic technology approaches was not part of routine care, which may contribute to inequities in provision of technologies to enhance occupational possibilities and meaningful activities in everyday lives of citizens with MCI/D.Trial registration: NSD project number 47996.Keywords: older adults, community health care services, discretion, street-level bureaucracy
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".