A Gig mHealth Economy Framework: Scoping Review of Internet Publications
Bibliographic record
Abstract
BACKGROUND: The gig economy (characterized by short-term contracts rather than being a full-time employee in an organization) is one of the most recent and important tendencies that have expanded through the global economic market thanks to advances in internet and communication technologies. Similarly, mobile health (mHealth) technologies have also evolved rapidly with the development of the internet and mobile apps, attracting attention globally for their health care benefits. OBJECTIVE: This study aimed to propose an integration of mHealth within the framework of the gig economy that leads to a new dimension of health care services and the proposal of a new term: gig mHealth. METHODS: A review and systematic search of articles, books, and opinions that allowed for answering the research questions were executed through the internet. In this sense, the concept of the gig economy and examples, advantages and disadvantages, were reviewed. Similarly, the general characteristics of mHealth technologies were revised. In addition, the role of technology in supporting the development of the gig economy and mHealth technologies and the interactions between them were investigated. RESULTS: The findings suggested that the gig economy is characterized by its flexibility in working hours, on-demand work, free agents, freelancing, freedom in the choice of work, and independent contracts. In addition, an analysis of an mHealth system indicated that it was composed of patients, specialists, nurses, and database administrators. In this system, patients and specialists or nurses are connected to cloud services for the transmission of data and medical information through a mobile app. Here, the administrators update the database and app features, among other technical tasks. Conversely, a general structure of an integrated gig mHealth system was developed. In this structure, the mHealth care services and the mHealth care activities were incorporated into a gig economy model. In addition, a practical example of an integrated view of a gig economy app in mHealth that illustrates the interaction between the patients (consumers) and providers (partners) of mHealth care services, mHealth care activities, health care professionals, and individual contractors was presented. The consumers and providers were interconnected with the health care company, brand, or firm through digital means using a mobile app or Windows platforms. CONCLUSIONS: The analysis carried out in this study suggested the possibility of integrating mHealth within the framework of the gig economy enhancing health care service delivery and the management of health care activities. The following 4 major areas of apps proposed in the mHealth framework that can catalyze the operations using the features of the gig economy were sharing/renting medical and diagnostic equipment and resources, on-demand appointments/self-health management, on-demand health care services, and assigning health care activities/gigs to individual contractors. This integration leads to a new dimension for health care services and the proposal of a new term: gig mHealth.
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 | 0.027 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.074 | 0.065 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".