Bibliographic record
Abstract
Abstract The purpose of this paper is to suggest a set of guidelines for the outsourcing of information systems services in health organizations. The paper shows that the outsourcing of the development of information systems is an option for hospitals in developing countries. It is emphasized that managers and information systems professionals working for these public organizations should bear in mind that what is important is to define the content, extent and length of the outsourced services. This paper offers a new angle, since it is often the case that hospital information systems in developing countries are either bought overseas or developed in house. The guidelines presented in this paper are derived from a case study conducted in the Ministry of Health of Guatemala. The guidelines originated from discussions among national authorities and national experts on how to manage the introduction of information technology and information systems to support the management of the hospitals. The paper concerns how the authorities and managers of the two major hospitals in the country decided to outsource key components of the information infrastructure and information systems of their hospitals. The paper also highlights the role of international agencies in funding the initiatives. The guidelines suggest that in outsourcing information services in a context similar to that of the case study, it is crucial to keep a balance between what type of expertise and knowledge the hospitals are willing to outsource and which areas of expertise should be kept in house. The challenge here is to match the ideal with what is feasible; this is the case particularly when the information technology comes from loans provided by international agencies. This means that when the period of the loan finishes, hospitals will need to take financial responsibility over the information systems. Hence the relevance for the hospitals to incorporate, along with outsourcing initiatives, sound financial and human resources strategies. The paper discusses how the hospitals under study tackled this issue. Other findings point out the need of including users and authorities of the hospitals in the design and identification of information requirements. The design of the new systems should not entail a radical change in the way people perform their jobs. Our analysis of the case study hints that the closer the design of the information system is to the current work practices the higher the likelihood that the users will accept the system. Changes in work practices may be gradually introduced after the system has been implemented.
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 | 0.082 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".