Summary
This blog shares findings from a new study comprising of two parts. Part one outlines a typology of profiles of adolescent reported protective factors in relation to mental well-being and the risk of mental disorder, using qualitative data. Part two applied the typology to identify trajectories of change in type membership occurring over one year, based on adolescent reports.
Background: The “what?”
Research suggests that there is an increased risk of psychopathology and lower mental well-being in early (10-14 years old) to mid-adolescence (14-16 years old) in the UK (Deighton et al., 2020; Pitchforth et al., 2019). In this context, it is helpful to understand what factors early adolescents themselves perceive to both increase their mental well-being and reduce the negative effects of risk factors for mental health difficulties (protective factors), as well as the factors they perceive to reduce their mental well-being (risk factors).
The evidence base on protective factors in relation to increased mental well-being and reduced risk of psychopathological outcomes is extensive (Fritz, et al., 2018; Eriksson et al., 2010; Wille et al., 2008). However, despite a wide range of protective factors studied in the literature, surprisingly, there is a lack of research on the lived experience of protective factors for British adolescents. Furthermore, there is a gap in the evidence of how these subjective reports and experiences change over time. Whilst there are a range of typologies of protective factors, most of these typologies use quantitative methods (Copeland-Linder, Lambert, & Ialongo, 2010; Solberg et al., 2007). Taking a qualitative approach to form a typology using ideal type analysis is unusual in studies of protective factors for mental well-being and reduced risk of mental health difficulties.
It is important to listen to young people’s narratives to understand the risk of poor well-being and the risk of mental health disorder, this is an underutilised approach.
A qualitative approach: Ideal type analysis
For the task of forming a typology of profiles of reported protective factors, this study applied ideal type analysis (Gerhardt, 1994). Data was drawn from semi-structured interviews with adolescents receiving HeadStart interventions from the first timepoint of a 5-year qualitative longitudinal study (May to July, 2017). HeadStart aims to explore and test new ways to improve the mental health and well-being of children and adolescents aged 10 to 16 (Stapley et al., 2019). At Time 1, participants’ ages ranged from 9.10 to 12.9 years (M = 11.49, SD = 0.92).
Ideal type analysis involves forming general categories from detailed individual cases (Livesley, 1991). It is based on the ideas of Weber (1904/1949) and applied within psychotherapeutic research (Gerhardt, 1994; Werbart et al., 2011). Ideal type analysis involves writing case reconstructions (summaries) which are descriptions of relevant content from each transcript and forming types to examine similarities and differences.
To conduct the analysis, risk and protective factors were systematically selected from each transcript and condensed into summaries (N =63 in Time 1). Below is an extract from a summary (please note all identifying details have been altered and the name is a pseudonym):
“Elliot” described that his family were waiting on statutory benefits and for a new job for his father. Elliot explained that his mother is not able to work because of her disability and there is a level of financial stress at home. At the time of interview, his mother had received phone calls at home from the school regarding his behaviour in class. Elliot explained that his mother gets frustrated by his behaviour at school and cannot easily come into school (as requested) due to her disabilities. He then explained to the interviewer that he would like to help his parents with their financial stress but feels he is unable to and that it may risk “making things worse”. Elliot discussed that he copes with the situation at home through activities outside of the house such as cycling with his friends, he explained: “I just, go on my bike with my mates, ride around, go places and just muck around.” When asked what he would do if he had a problem, he reported that he would not approach an adult: “cause they wouldn’t listen”.
Each summary was then sorted based on similarities and differences to other cases in the sample until general categories were constructed. A second researcher then sorted the cases using the type descriptions. A research assistant also sorted the cases using the descriptions and a Young Advisor from a Young People’s coproduction organisation reviewed the language of the types and descriptions.
Reports of protective factors by young people can show how mental health and well-being deteriorates, improves or remains the same over time.
The typology
The three ideal types of reported protective factors in relation to risk and mental being are provided below, with the number of adolescents allocated to each type in Table 1.
- The adolescent with ‘Uncertain Sources of Support’ (USS). Adolescents in this type reported a range of risk factors (e.g. parental illness, socio-economic difficulties, bullying, behaviour difficulties) but the support in relation to the risk factors was uncertain or ineffective. In relation to difficulties, they may have reported an absence of coping or maladaptive coping strategies, with some reports of poor mental well-being.
- The adolescent with ‘Self-Initiated Forms of Support’ (SIFS). Adolescents in this type reported some risk factors, but drew on their own inner resources, such as problem-solving to address them, rather than seeking support from others. These adolescents did not emphasise the role of school or parents in their protective factors. In this type there were some reports of positive well-being.
- The adolescent with ‘Multiple Sources of Support’ (MSS). Adolescents in this type reported a range of effective support from parents, school, friends and community (such as sports clubs or religious membership). Many of these young people reported some risk factors but described effective support in relation to them. Some adolescents reported an absence of problems or generally positive mental well-being (Eisenstadt, Stapley & Deighton, 2020).
Table 1
Number of participants in the three ideal types at Time 1
| The adolescent with ‘Uncertain Sources of Support’ | The adolescent with ‘Self-Initiated Forms of Support’ | The adolescent with ‘Multiple Sources of Support’ | |
| Females | 17 | 2 | 9 |
| Males | 18 | 5 | 12 |
| Total | 35 | 7 | 21 |
Part two
To explore change in type membership over time, data was analysed from Time 2 of the longitudinal study. In the Time 2 sample, participants’ ages ranged from 10.07 to 13.90 years (M = 12.35, SD = 2.95), with one instance of missing age data. Ideal type analysis was applied to analyse the transcripts from Time 2 (N = 60), with 3 cases of missing data. Cases were sorted blind to previous allocations using the typology to assign type membership. A second researcher also sorted the types blind. Differences in allocation between myself and the second researcher were resolved by a third researcher.
Results
Participants that were previously allocated into three types from Time 1 were compared with the allocated type at Time 2. This yielded 9 subtypes based on change in type membership. The subtypes of participants that remained unchanged included: USS-USS, SIFS-SIFS and MSS-MSS. The 6 transitional types comprised of: USS-SIFS, USS– MSS, SIFS-USS, SIFS-MSS, MSS- USS and MSS- SIFS. Table 2 shows the difference in the numbers of each type across the two timepoints and direction towards more effective protective factors reported, or towards less effective protective factors (or their absence) reported.
Table 2
Numbers of adolescents in each ideal type (USS, SIFS, MSS) in Time 1 and Time 2 and direction of overall positive or negative shift towards more effective support
| Ideal type | T1 | T2 | Difference | Direction |
| Uncertain Sources of Support | 35 | 16 | -19 | decrease |
| Self-initiated Forms of Support | 7 | 12 | +5 | increase |
| Multiple Sources of Support | 21 | 32 | +11 | increase |
| Total | 63 | 60 |
Note. Totals include all subtypes of type in Time 2. For example, USS in T2 includes the subtypes: USS-USS, SIFS-USS, and MSS-USS.
Going forward: What are the implications?
This typology provides an additional approach to identify adolescents at risk of poor mental well-being or poor mental health. According to the cumulative risk hypothesis, individuals that have most risk factors and least protective factors are most at risk of psychopathology (Evans & English, 2002; Evans, Lee & Whipple, 2013).
In this context, it is helpful to understand what factors early adolescents themselves perceive to increase their mental well-being and reduce the negative effects of risk factors for mental health difficulties (protective factors), as well as the factors they perceive to reduce their mental well-being (risk factors).
Conclusions – Final thoughts
Studying adolescent reports over time using ideal type analysis, can show both positive and negative changes and stability of protective factors as per the typology described in this study over one year. On a practical level, adolescent self-reports of gaps in protective factors in relation to risk (such as a reported lack of support and poor coping) provides an adolescent-centred approach to identification of risk for poor mental health and wellbeing.
Understanding adolescent’s perspectives on risk and protective factors towards the stressors that they perceive to affect their mental well-being is important in the design and iteration of preventative interventions.
Primary paper
A qualitative study of changes in constellations of reported protective factors in relation to mental well-being and the risk of psychopathology over the course of one year for 10-14-year olds (Submitted for publication).
Conflicts of interest
No conflicts of interest to report.
Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this blog: The data used in this study were collected as part of HeadStart Learning Programme and supported by funding from The National Lottery Community Fund. The content is solely the responsibility of the authors and it does not necessarily reflect the views of The National Lottery Community Fund.
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Websites
https://www.ucl.ac.uk/evidence-based-practice-unit/headstart-learning-team
