Impact of Digital Inclusion Initiative to Facilitate Access to Mental Health Services: Service User Interview Study
Bibliographic record
Abstract
BACKGROUND: Digital exclusion, characterized by a lack of access to digital technology, connectivity, or digital skills, disproportionally affects marginalized groups. An important domain impacted by digital exclusion is access to health care. During COVID-19, health care services had to restrict face-to-face contact to limit the spread of the virus. The subsequent shift toward remote delivery of mental health care exacerbated the digital divide, with limited access to remote mental health care delivery. In response, Camden and Islington National Health Service Foundation Trust launched the innovative Digital Inclusion Scheme (DIS). OBJECTIVE: This study aimed to examine the impact of facilitating digital inclusion in mental health access. Camden and Islington National Health Service Foundation Trust implemented the trust-wide DIS for service users who were digitally excluded, that is, were without devices or connectivity or reported poor digital skills. The scheme provided access to a loan digital device (a tablet), internet connectivity devices, and mobile data, as well as personalized digital skills support. METHODS: The DIS went live in October 2021 and received 106 referrals by June 2022. Semistructured interviews were conducted with 12 service users to ask about their experience of accessing the DIS. A thematic analysis identified themes and subthemes relating to the extent of their digital exclusion before engaging with the scheme and the impact of accessing a scheme on their ability to engage with digital technology and well-being. RESULTS: There were 10 major themes. A total of 6 themes were related to factors impacting the engagement with the scheme, including digital exclusion, relationship to the trust, the importance of personalized digital support, partnership working, device usability and accessibility, and personal circumstances. The remaining 4 themes spoke to the impact of accessing the scheme, including improved access to services, impact on well-being, financial implications, and a greater sense of empowerment. CONCLUSIONS: Participants reported an increased reliance on technology driving the need for digital inclusion; however, differences in motivation for engaging with the scheme were noted, as well as potential barriers, including lack of awareness, disability, and age. Overall, the experience of accessing the DIS was reported as positive, with participants feeling supported to access the digital world. The consequences of engaging with the scheme included greater perceived access to and control of physical and mental health care, improved well-being, and a greater sense of empowerment. An overview of the lessons learned are provided along with suggestions for other health care settings that are looking to implement similar schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".