DEVELOPMENT AND VALIDATION OF THE SRI LANKAN PRIMARY CARE ASSESSMENT TOOL (SL-PCAT) FOR EVALUATING PATIENTS' EXPERIENCES IN PRIMARY CARE

This study successfully developed and validated the Sri Lankan Primary Care Assessment Tool (SL-PCAT) as a feasible, reliable, and valid instrument for evaluating patients' experiences in primary care within the Sri Lankan healthcare setting.

The Gist

Imagine you have a car, and you want to know if it's a good vehicle. You wouldn't just look at the shiny paint; you'd take it for a test drive, check the engine, see how the brakes feel, and ask the driver how the ride was.

This paper is essentially the story of building a special "test drive" report card for Sri Lanka's primary healthcare system. The researchers wanted to create a tool to measure how patients actually feel when they visit their local doctor, rather than just guessing. They called this new tool the SL-PCAT.

Here is how they built and tested it, using some simple analogies:

1. Designing the Blueprint (The Methods)

Think of the researchers as architects. Before building a house, they need a solid plan.

• Gathering Ideas: They didn't just guess what questions to ask. They held "town hall meetings" (focus groups) with regular people, read old blueprints (literature reviews), and asked master builders (experts) for advice.
• Translation: Since the tool needed to be used locally, they had two expert translators turn the English questions into Sinhala, ensuring the meaning didn't get lost in translation—like making sure a recipe tastes the same whether you cook it in London or Colombo.
• The Test Drive: They took this new questionnaire to 32 villages in the Anuradhapura district and asked 633 people to fill it out.

2. Sorting the Puzzle Pieces (The Analysis)

Once they had all the answers, they had a giant box of puzzle pieces (data). They needed to see if the pieces fit together to make a clear picture.

• Splitting the Group: To be extra sure, they split the 633 people into two teams.
  • Team A (320 people): They used this group to discover the pattern. Imagine looking at a messy pile of LEGOs and realizing, "Oh! These red ones make a wheel, and these blue ones make a roof." This is called Exploratory Factor Analysis.
  • Team B (313 people): They used this group to prove the pattern was right. They checked if the LEGOs they sorted in Team A actually built the same house when Team B tried to build it. This is called Confirmatory Factor Analysis.

3. The Four Pillars of Good Care (The Results)

The "puzzle" they solved revealed that a good primary care experience rests on four main pillars (or domains):

1. Contextual Care: It's like a doctor who knows your family history and your neighborhood, not just your symptoms. They see the whole picture.
2. Accessibility: Can you actually get to the doctor? Is it easy to make an appointment?
3. Patient-Centered Care: Does the doctor listen to you and treat you like a human being, not just a number?
4. Comprehensive & Coordinated Care: Does the doctor have all the tools to fix the problem, and do they work well with other specialists if needed?

4. Did the Tool Work? (The Validation)

The researchers ran the numbers to see if the tool was reliable.

• The "Sturdiness" Test: They checked if the questions in each pillar were consistent. The scores were high (between 0.72 and 0.86), which is like saying, "Yes, this bridge is strong enough to hold traffic."
• The "Fit" Test: They checked how well the math matched reality. The results showed the tool fits the Sri Lankan context very well, with only a few tiny tweaks needed to make it perfect.
• The Verdict: Most people gave positive scores, meaning they generally felt their care was good. The tool successfully distinguished between different types of care experiences.

The Bottom Line

The SL-PCAT is now a ready-to-use, reliable "report card" for Sri Lanka. Just as a mechanic uses a diagnostic tool to fix a car, health officials can now use this tool to diagnose how well the primary healthcare system is working and fix any "flat tires" or "engine trouble" to ensure every patient gets the best ride possible.

Technical Summary

Technical Summary: Development and Validation of the SL-PCAT

1. Problem Statement

The study addresses the critical lack of a culturally and contextually appropriate instrument to evaluate patients' experiences within the primary healthcare system of Sri Lanka. While primary care assessment tools exist globally, there was a need for a validated tool specifically tailored to the Sri Lankan healthcare setting, language (Sinhala), and socio-cultural context to accurately measure service quality and patient satisfaction.

2. Methodology

The research employed a rigorous mixed-methods approach spanning from April 2021 to August 2023, conducted within a medical faculty and selected communities in the Anuradhapura District. The development and validation process followed a structured sequence:

• Item Generation: Tool items were derived from a triangulation of focused-group interviews, a comprehensive literature review, and expert reviews.
• Translation: The instrument was translated into Sinhala by two language experts to ensure linguistic accuracy.
• Data Collection: A cross-sectional household survey was conducted across 32 villages, yielding 633 valid responses.
• Statistical Analysis Strategy:
  • Data Splitting: The dataset was randomly divided into two subsamples: Subsample-1 (n=320) for exploratory analysis and Subsample-2 (n=313) for confirmatory analysis.
  • Exploratory Factor Analysis (EFA): Performed on Subsample-1 using Principal Component Analysis (PCA) to identify the underlying factor structure. Reliability was assessed using Cronbach's alpha.
  • Confirmatory Factor Analysis (CFA): Conducted on Subsample-2 to validate the factor structure derived from the EFA. Model fit was evaluated using indices including Chi-square ($\chi^2$), RMSEA, GFI, SRMR, and CFI.
  • Validity Checks: Convergent and discriminant validity were tested via item-scale correlations and scaling success rates. Descriptive statistics and Likert scale assumption testing were applied to the final structure.

3. Key Contributions

• Development of SL-PCAT: The creation of the Sri Lankan Primary Care Assessment Tool (SL-PCAT), the first validated instrument of its kind for the region.
• Identification of Four Core Domains: The study established a four-factor structure specific to the local context:
  1. Contextual care at family and community with continuity.
  2. Accessibility.
  3. Patient-centred care.
  4. Comprehensive and coordinated care.
• Methodological Rigor: The study provides a robust statistical framework for tool validation in low-resource settings, demonstrating the successful application of EFA, CFA, and reliability testing in a developing nation's healthcare context.

4. Key Results

• Factor Structure: The PCA revealed a four-factor solution that explained 66.8% of the total variance in the data.
• Reliability: The internal consistency was strong, with Cronbach's alpha values ranging from 0.719 to 0.859 across the identified domains.
• Model Fit (CFA): The confirmatory model demonstrated good fit after minor modifications:
  • $\chi^2 = 199$ ($p < 0.001$)
  • RMSEA = 0.079
  • GFI = 0.916
  • SRMR = 0.066
  • CFI = 0.916
• Validity: The tool exhibited optimum convergent and discriminant validity. Item-scale correlations ranged between 0.346 and 0.830, and scaling success rates exceeded 70%.
• Response Distribution: Three of the four domains showed positively skewed scores, suggesting that patients generally reported positive experiences, with the exception of "contextual care at family and community with continuity."

5. Significance

The SL-PCAT is confirmed as a feasible, valid, and reliable instrument for assessing patient experiences in Sri Lankan primary care. Its significance lies in:

• Policy and Quality Improvement: Providing health policymakers and administrators with a standardized metric to monitor service quality, identify gaps, and drive targeted improvements in primary care delivery.
• Research Utility: Offering a validated tool for future epidemiological studies and comparative research on primary care in South Asia.
• Cultural Relevance: Ensuring that patient feedback is captured in a manner that respects local linguistic and cultural nuances, thereby increasing the accuracy of patient-reported outcome measures (PROMs) in the region.