Using the PMAQ-AB Mobile App and Management System to Evaluate the Quality of Primary Health Care in Brazil: Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: The application of cell phones, similar portable devices (ie, tablets), apps, the internet, and GPS in evaluation have established new ways of collecting, storing, retrieving, transmitting, and processing data or information. However, evidence is incipient as to which technological resources remain at the center of assessment practice and the factors that promote their use by the assessment community. OBJECTIVE: This study aimed to analyze the relationship between the use of the National Program for Improving Primary Healthcare Access and Quality's (PMAQ-AB; Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica) mobile app and management system and the external evaluation quality of Brazil's PMAQ-AB. METHODS: We conducted a qualitative case study during the external evaluation of Brazil's PMAQ-AB. Data collection consisted of interviews, focus groups, and document analysis. A total of 7 members from the Department of Primary Care of the Ministry of Health and 47 researchers from various higher education and research institutions across the country participated in the study. Data were categorized using the ATLAS.ti software program, according to the quality standards of the Joint Committee on Standards for Educational Evaluation, following the content analysis approach by Bardin. RESULTS: The results related to feasibility, thematic scope, field activity management, standardized data collection, data consistency, and transparency. They demonstrated improvements and opportunities for advancements in evaluation mediated by the use of information technology (IT), favored the emergence of new practices and remodeling of existing ones, and took into account the multiple components required by the complex assessment of access and quality in primary health care. Difficulties in technology operation, inoperative systems, and lack of investment in equipment and human resources posed challenges to increasing the effectiveness of IT in evaluation. CONCLUSIONS: The use of technology-based tools-the app and the management system-during the external evaluation offered evaluators a greater opportunity for stakeholder engagement. This also allowed the insertion of different organizational, operational, and methodological components that are capable of triggering influences and confluences. In addition, this allowed connections in collaborative and synergistic networks to increase the quality and allow the development of a more consistent and efficient evaluation process with greater possibility of incorporating the results into public health policies.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".