A1. Appropriate qualitative data gathering method

Focus groups can be the most suitable qualitative research data collection technique. Focus groups enable the gathering of rich information regarding the attitudes, perceptions and experiences of at-risk youth (Pavez et al., 2024). It allows endeavours of discussions to be effective and engaging as every participant is not only allowed to make their points but also gets to expand on everyone else’s point of view as well (DeSouza et al., 2024). It enables the definition of common aspects as well as differences and encumbrances, especially in diverse and disadvantaged communities. With trained facilitators, it is easier to manage discussions and ensure they address the youth’s needs and experiences as they are. Also, focus groups are relatively cheap and can be used to gather as many people’s views as needed in the shortest time possible, which will help to inform the development of the mental health strategy.

A2: Verification strategies

To achieve credibility and dependability of qualitative data collected during the study, some verification techniques are important during the design and operations stage.

• Methodological coherence

Methodological coherence requires alignment between research questions, methods, and data collection techniques. This makes the study coherent and moves systematically from one point to the other (Luke & Goodrich, 2019). In the scenario, it is helpful to use focus groups to capture the youth perspectives to meet the goal of identifying commonalities (Luke & Goodrich, 2019). Clearly defined aims, the formulation of guidelines for the discussion and their preliminary testing guarantee that the chosen approach covers all aspects of the research questions.

• Sample selection

A purposive sampling helps in the inclusion of diversified young people especially those who are most vulnerable. The approach allows for representation of diverse issues of socioeconomic, ethnic and gender diversity (Gandy, 2024). Recruitment should incorporate schools, community-based organizations and social media platforms to increase the population to be reached (Muijeen et al., 2020). There are key guidelines that sampling criteria should state the inclusion of participants in the sample size, relevance to the research questions, and coverage of all the different categories.

• Theoretical thinking

Theoretical thinking entails the employment of theory in order to direct the process of data gathering and analysis. It is possible to use both grounded theory and thematic analysis to look for patterns and make conclusions from them (Naeem et al., 2023). To this end, the enhancement of social determinants of health or resilience mandates assist youth’s experiences’ interpretation bearing in mind the broader systemic and personal settings. When performing the analysis, both reflective journaling and team discussions improve the theoretical coherence and reliability.

• Appropriate analysis and presentation

Another reason for the use of thematic analysis is to make sure that qualitative data is analyzed methodically in a way to determine purely thematic indicators. Thematic coding should be done by more than one researcher to eliminate bias and enhance inter-coder reliability (Jowsey et al., 2021). Recording of decisions made during the analysis of data helps in maintaining transparency hence credibility. Including quotations alongside theme analysis makes the evidence more authentic, and contextually richer than is the case when only themes are presented.

Such strategies are integrated to achieve methodological credibility and to produce valid and convincing evidence. This rigour increases the applicability of the results with the Comprehensive Youth Mental Health Strategy for youth subpopulations in Alberta.

Part B: Measurement of public health constructs

B1: Mixed methods approach to measuring anxiety in youth

The assessment of anxiety is feasible using both quantitative and qualitative research methods. Surveys quantify preselected stressors and coping strategies; quantitatively, focus groups or interviews evaluate stress experiences, coping, and unmet needs in youth (Cheetham‐Blake et al., 2019). For instance, questions like “Can you tell me some scenarios that may trigger anxiety?” are such questions that bring out context and real-life experiences. This is in addition to the measurement of severity and prevalence using standardised, validated rating scales such as the Generalized Anxiety Disorder-7 (GAD-7) (Sapra et al., 2020). Quantitative questions are questions like “In the last two weeks how often did you feel nervous or anxious?” For this reason, these approaches are relevant to apply in parallel to collect all the necessary data, including numerical preferences and case histories. The analysis of quantitative outcomes by incorporating qualitative data is valuable in the study of youth anxiety in the study (Hao et al., 2020). It identifies trends as it deals with different experiences to offer scientific advice to the Comprehensive Youth Mental Health Strategy. Therefore, mixed methods can reveal the variety of aspects related to anxiety in youths from the point of view of different fields and provide an opportunity to compare numbers to narratives.

B2: Addressing systematic measurement errors in surveys

Inaccuracy in measurement procedures can distort the results of survey research. Possible sources and measures to minimize errors ensure sound information for the Comprehensive Youth Mental Health Strategy.

• Response Bias

Response bias occurs when the respondents provide acceptable answers, not real answers (Meisters et al., 2020). Consequently, youth do not necessarily reveal high anxiety because of the shame or perception they have of what they are required. This bias is always possible, which makes it logical that the survey will be anonymous, and its results will remain confidential (Kelly et al., 2020). Other possible ways to minimize response bias are: The skills of not making the questions leading and not using a specific tone of voice, too.

• Recall Bias

Recall bias can be attributed to the fact that some of the respondents may not be able to recall their experiences and thus give half-baked information (Moreno-Serra et al., 2022). This is especially the case when the survey is about the frequency of the symptoms of anxiety They found that different forms of anxiety are related to different types of thought patterns, and therefore can be managed differently. To prevent recall bias, it is preferable to avoid questions which imply that some event has occurred during a long period, for example, the usage of the title “In the past week,” instead of “In the past month.” It is also possible to help the memory recall by providing examples or clarification with the questions of the survey.

• Sampling Bias

One of the usual types of sampling bias is where the population of the survey is not diverse enough to be drawn from the target population (Mäkinen et al., 2023). For instance, choosing many participants from the urban setting may lead to the exclusion of data from rural participants. For this reason, there is the need to adopt the method of stratified random sampling in a way that will consider the proportion of age, geographical location and socioeconomic status of the population being studied. It increases sample heterogeneity because recruitment is done through various media and also reduces the risk of sampling.

• Instrument Bias

In the survey, measurement bias occurs when the instruments used fail to provide the correct or more accurate measurement of the intended construct (Clayton et al., 2023). For example, using unreliable measuring tools like scales leads to wrong results of anxiety. To overcome this issue, one should ensure that they employ the GAD-7 and thus ensure that the survey measures anxiety across all patients to an equal level. It can be used in determining the latter because pilot testing can often unveil question ambiguities.

Thus, by handling sources of systematic erroneous results identified above, the survey results will reflect the mental health concerns of young people in Alberta to an extent. They enhance the reliability of the evidence in support of the Comprehensive Youth Mental Health Strategy.

Part C: Survey

C1: Sampling plan for naloxone training survey

It is recommended that a probability sampling method called stratified random sampling should be used to sample Alberta post-secondary medical students. It makes it possible to have a good sample that will represent the target population by developing other layers or groups that are unique from other groups depending on factors such as the type of institution (university and college), or region (urban or rural) (Howell et al., 2020). In each stratum, the sample is then taken randomly to have some sort of control over subgroups in the given stratum (Howell et al., 2020). The strength of the method is to increase precision and to reduce sampling bias by including all second and further-order sub-populations of the total population in the sample (Zaman, 2020). For instance, it ensures that anyone who has a chance to attend discriminated smaller institutions or distant regions is given a chance to give their input that will be considered. Moreover, the stratified sampling allows comparison to be made of the outcome of the various sub-samples, for instance between rural and urban students.

However, this approach needs accurate population data to define the strata and allocate a proportionate sample from each of the strata which may add some degree of complication and be time-consuming (Howell et al., 2020). However, the advantages of this capability to provide statistically valid and generalizable results eclipse this potential difficulty. Therefore, the present research established stratified random sampling that guarantees the capture of a balanced dataset regarding Alberta’s medical students to conduct the assessment of the necessity of naloxone training interventions. It enhances the use of scientific data in decision-making processes and embraces the public in planning processes in the health sector.

C2: Strategies to reduce non-response errors

Since non-response errors are unavoidable in the survey, it is necessary to apply special measures to increase the response rate and the quality of collected data.

• Personalized communication

When invitations are personalized, one is more likely to respond than when the survey invitation is generic since they feel that the survey is very important to them (Luiten et al., 2020). The use of emails or letters that are written directly to the participants, where they are told how their contribution can affect naloxone training, helps to increase their interest and hence their commitment.

• Incentives

Rewarding participation, for instance, offering participants small amounts of cash or gift cards motivates the participants. Other incentives that are often used include offering respondents’ gifts such as entry into a draw to win a prize or being part of a recognized community such as students.

• Multiple distribution of the questionnaire

Having online, paper and telephone as ways of responding helps to address various needs and provide equal opportunity (Zimba & Gasparyan, 2023). It is especially useful for getting to those participants who may not have internet access or who prefer using email.

• Follow-up reminders

The use of follow-up reminders at predetermined time intervals assists in cases where participants forget to respond while those who respond at last are prompted to do so. Such a message should also be a reminder of the survey’s deadline and make it more compelling (Moreno-Serra et al., 2022).

These strategies can correct non-response errors since they embrace the same population to ensure that they are represented. Individualised messages and rewards create first interest while reaching out with several channels and follow-up messages maintain interest. Combined, these approaches increase response rates to make the survey results useful and accurate for planning naloxone training interventions.

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